Let's learn about Machine Learning via these 500 free blog posts. They are ordered by HackerNoon reader engagement data. Visit the Learn Repo or LearnRepo.com to find the most read blog posts about any technology.
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1. 13 Best Datasets for Power BI Practice
In 2022, Gartner named Microsoft Power BI the Business Intelligence and Analytics Platforms leader. These are the 13 Best Datasets for Power BI Practice.
2. Why Is GPT Better Than BERT? A Detailed Review of Transformer Architectures
Details of Transformer Architectures Illustrated by BERT and GPT Model
3. My Self-Created Artificial Intelligence Masters Degree
<strong><em>Note:</em></strong> This article is a perpetual work in progress and is up to date as of 17 May 2019.
4. Deep Learning vs. Machine Learning: A Simple Explanation
Machine learning and deep learning are two subsets of artificial intelligence which have garnered a lot of attention over the past two years. If you’re here looking to understand both the terms in the simplest way possible, there’s no better place to be.
5. Gradient Descent: All You Need to Know
What’s the one algorithm that’s used in almost every Machine Learning model? It’s <strong>Gradient Descent</strong>. There are a few variations of the algorithm but this, essentially, is how any ML model learns. Without this, ML wouldn’t be where it is right now.
6. Why businesses fail at machine learning
I’d like to let you in on a secret: when people say ‘<a href="http://bit.ly/quaesita_simplest" target="_blank">machine learning</a>’ it sounds like there’s only one discipline here. There are two, and if businesses don’t understand the difference, they can experience a world of trouble.
7. 6 Best Open-Source Projects for Real-Time Face Recognition
8. How I built a spreadsheet app with Python to make data science easier
Today I'm open sourcing "Grid studio", a web-based spreadsheet application with full integration of the Python programming language.
9. The simplest explanation of machine learning you’ll ever read
You’ve probably heard of machine learning and <a href="http://bit.ly/quaesita_ai" target="_blank">artificial intelligence</a>, but are you sure you know what they are? If you’re struggling to make sense of them, you’re not alone. There’s a lot of buzz that makes it hard to tell what’s science and what’s science fiction. Starting with the names themselves…
10. How To Plot A Decision Boundary For Machine Learning Algorithms in Python
Classification algorithms learn how to assign class labels to examples (observations or data points), although their decisions can appear opaque.
11. Decoding Transformers' Superiority over RNNs in NLP Tasks
Explore the intriguing journey from Recurrent Neural Networks (RNNs) to Transformers in the world of Natural Language Processing in our latest piece: 'The Trans
12. Crossentropy, Logloss, and Perplexity: Different Facets of Likelihood
We explore the link between three popular loss functions: crossentropy, logloss and perplexity
13. A list of artificial intelligence tools you can use today — for personal use (1/3)
Artificial Intelligence and the fourth industrial revolution has made some considerable progress over the last couple of years. Most of this current progress that is usable has been developed for industry and business purposes, as you’ll see in coming posts. Research institutes and dedicated, specialised companies are working toward the ultimate goal of AI (cracking artificial general intelligence), developing open platforms and the looking into the ethics that follow suit. There are also a good handful of companies working on AI products for consumers, which is what we’ll be kicking this series of posts off with.
14. Top 5 Machine Learning Projects for Beginners
As a beginner, jumping into a new machine learning project can be overwhelming. The whole process starts with picking a data set, and second of all, study the data set in order to find out which machine learning algorithm class or type will fit best on the set of data.
15. How to Talk to ChatGPT: An Intro to Prompt Engineering
Prompting is pretty much the only skill you now require to be a master of these new large and powerful generative models such as ChatGPT.
16. ChatGPT Explained in 5 Minutes
ChatGPT has taken over Twitter and pretty much the whole internet, thanks to its power and the meme potential it provides.
17. Deep Learning CNN’s in Tensorflow with GPUs
18. Search Algorithms in Artificial Intelligence
There can be one or many solutions to a given problem, depending on the scenario, As there can be many ways to solve that problem. <strong><em>Think about how do you approach a problem.</em></strong> Lets say you need to do something straight forward like a math multiplication. Clearly there is one correct solution, but many algorithms to multiply, depending on the size of the input. Now, take a more complicated problem, like playing a game(imagine your favorite game, chess, poker, call of duty, DOTA, anything..). In most of these games, at a given point in time, you have multiple moves that you can make, and you choose the one that gives you best possible outcome. In this scenario, there is no one correct solution, but there is a best possible solution, depending on what you want to achieve. Also, there are multiple ways to approach the problem, based on what strategy you choose to have for your game play.
19. 160+ Data Science Interview Questions
A typical interview process for a data science position includes multiple rounds. Often, one of such rounds covers theoretical concepts, where the goal is to determine if the candidate knows the fundamentals of machine learning.
20. Introducing Drag Your GAN: Drag Objects to Create New Images
This isn’t just editing, but actually the creation of completely new images, allowing you to change object positions, subject poses, and more.
21. I Conducted Experiments With the Alpaca/LLaMA 7B Language Model: Here Are the Results
I set out to find out Alpaca/LLama 7B language model, running on my Macbook Pro, can achieve similar performance as chatGPT 3.5
22. Real-world Use Cases of Dynamic Programming
Applications of dynamic programming
23. 10 Best Datasets for Time Series Analysis
In order to understand how a certain metric varies over time and to predict future values, we will look at the 10 Best Datasets for Time Series Analysis.
24. NLP Tutorial: Topic Modeling in Python with BerTopic
Topic modeling is an unsupervised machine learning technique that can automatically identify different topics present in a document (textual data). Data has become a key asset/tool to run many businesses around the world. With topic modeling, you can collect unstructured datasets, analyzing the documents, and obtain the relevant and desired information that can assist you in making a better decision.
25. We Built a Face and Mask Detection Web App for Google Chrome
Face and mask detection in browser using TensorFlow.js, openCV.js. Investigate results with different implementations.
26. How to Build a Web Scraper With Python [Step-by-Step Guide]
On my self-taught programming journey, my interests lie within machine learning (ML) and artificial intelligence (AI), and the language I’ve chosen to master is Python.
27. Audio Handling Basics: Process Audio Files In Command-Line or Python
Like my articles? Feel free to vote for me as ML Writer of the year here.
28. My Notes on MAE vs MSE Error Metrics 🚀
We will focus on MSE and MAE metrics, which are frequently used model evaluation metrics in regression models.
29. Capsule Networks Are Shaking up AI — Here’s How to Use Them
If you follow AI you might have heard about the advent of the potentially revolutionary Capsule Networks. I will show you how you can start using them today.
30. What is Image Annotation? – An Intro to 5 Image Annotation Services
Image annotation is one of the most important tasks in computer vision. With numerous applications, computer vision essentially strives to give a machine eyes – the ability to see and interpret the world. At times, machine learning projects seem to unlock futuristic technology we never thought possible. AI-powered applications like augmented reality, automatic speech recognition, and neural machine translation have the potential to change lives and businesses around the world. Likewise, the technologies that computer vision can give us (autonomous vehicles, facial recognition, unmanned drones) are extraordinary.
31. Rational Agents for Artificial Intelligence
There are multiple approaches that you might take to create Artificial Intelligence, based on what we hope to achieve with it and how will we measure its success. It ranges from extremely rare and complex systems, like self driving cars and robotics, to something that is a part of our daily lives, like face recognition, machine translation and email classification.
32. What are the Best Free AI Art Generators of 2023?
Generative AI has made groundbreaking strides in the past few months, and Generative AI models have risen in general popularity.
33. Automatic Feature Selection in Python: An Essential Guide
Feature Selection in python is the process where you automatically or manually select the features in the dataset that contribute most to your prediction.
34. Understanding the Two-Tower Model in Personalized Recommendation Systems
Understanding how the two-tower model is used for the retrieval stage of recommendation systems.
35. What is Python Good for? Why Beginner Should Learn Python?
Data science and machine learning are the two main things Python is perfect for. Code simplicity, higher salary, and automation are just some of the best reasons to Learn Python, if you're on the fence about it.
36. Understanding Stochastic Average Gradient
Techniques like Stochastic Gradient Descent (SGD) are designed to improve the calculation performance but at the cost of convergence accuracy.
37. Demystifying the Poisson Multi-Bernoulli Mixture Filter: A Leap Forward in Multi-Target Tracking
The primary task at hand revolves around multi-object tracking, a problem rooted in the complex and dynamic nature of real-world environments.
38. Top C/C++ Machine Learning Libraries For Data Science
Importance of C++ in Data Science and Big Data
39. A Basic Knowledge of Python Can Help You Build Your Own Machine Learning Model
Let's build our own Machine Learning Model with Tensorflow, a Python library.
40. Imagic: AI Image Editing from Text Commands
This week’s paper may just be your next favorite model to date.
41. Top 20 Image Datasets for Machine Learning and Computer Vision
Computer vision enables computers to understand the content of images and videos. The goal in computer vision is to automate tasks that the human visual system can do.
42. 7 Effective Ways to Deal With a Small Dataset
In a real-world setting, you often only have a small dataset to work with. Models trained on a small number of observations tend to overfit and produce inaccurate results. Learn how to avoid overfitting and get accurate predictions even if available data is scarce.
43. Developing AI Security Systems With Edge Biometrics
Let’s speak about usage of edge AI devices for office entrance security system development with the help of face and voice recognition.
44. 16 Best Sklearn Datasets for Building Machine Learning Models
Sklearn datasets are included as part of the scikit-learn (sklearn) library, so they come pre-installed with the library.
45. How to Install PrivateGPT: A Local ChatGPT-Like Instance with No Internet Required
A powerful tool that allows you to query documents locally without the need for an internet connection. Whether you're a researcher, dev, or just curious about
46. From Tasks to Thinking Systems: Why Automation Starts in the Mind, Not the Machine
A reflection on why true automation starts with human thinking, not technology. Systems only work as clearly as the minds that design them.
47. How to Structure Your Machine Learning Team for Success
This article discusses alternative ML team organizational models and recommendations for matching team structures to the company's stage of development.
48. THE BEST Photo to 3D AI Model !
As if taking a picture wasn’t a challenging enough technological prowess, we are now doing the opposite: modeling the world from pictures. I’ve covered amazing AI-based models that could take images and turn them into high-quality scenes. A challenging task that consists of taking a few images in the 2-dimensional picture world to create how the object or person would look in the real world.
49. Yet Another Lightning Hydra Template for ML Experiments
Flexible and scalable template based on PyTorch Lightning and Hydra. Efficient workflow and reproducibility for rapid ML experiments.
50. How To Build Chatbot Project Using Python
Chatbots are extremely helpful for business organizations and also the customers. The majority of people prefer to talk directly from a chatbox instead of calling service centers. Facebook released data that proved the value of bots. More than 2 billion messages are sent between people and companies monthly. The HubSpot research tells that 71% of the people want to get customer support from messaging apps. It is a quick way to get their problems solved so chatbots have a bright future in organizations.
51. 🎁 Releasing “Supervisely Person” dataset for teaching machines to segment humans
Hello, Machine Learning community!
52. Learning AI if You Suck at Math — Part Two — Practical Projects
53. How to Initialize weights in a neural net so it performs well?
We know that in a neural network, weights are initialized usually randomly and that kind of initialization takes fair / significant amount of repetitions to converge to the least loss and reach to the ideal weight matrix. The problem is, this kind of initialization is prone to vanishing or exploding gradient problems.
54. What is a Decision Tree in Machine Learning?
Decision trees, one of the simplest and yet most useful Machine Learning structures. Decision trees, as the name implies, are <a href="https://medium.com/brandons-computer-science-notes/trees-the-data-structure-e3cb5aabfee9" target="_blank">trees </a>of decisions.
55. Navigating the Vector Database Landscape
Learn about the options for vector databases and how each works.
56. NLP Datasets from HuggingFace: How to Access and Train Them
The Datasets library from hugging Face provides a very efficient way to load and process NLP datasets from raw files or in-memory data. These NLP datasets have been shared by different research and practitioner communities across the world.
57. Radial Basis Functions: Types, Advantages, and Use Cases
An introductory article explaining the basic intuition, mathematical idea & scope of radial basis function in the development of predictive machine learning.
58. Python for Data Science: How to Scrape Website Data via the Internet's Top 300 APIs
59. 10 Machine Learning, Data Science, and Deep Learning Courses for Programmers in 2020
A curated list of courses to learn data science, machine learning, and deep learning fundamentals.
60. Top 15 Chatbot Datasets for NLP Projects
An effective chatbot requires a massive amount of training data in order to quickly solve user inquiries without human intervention. However, the primary bottleneck in chatbot development is obtaining realistic, task-oriented dialog data to train these machine learning-based systems.
61. 3 Things to Consider Before Adding GenAI to Your Business
Secure the future of Your Business with GenAI. Consider 3 factors needed for successful deployment of GenAI in your business.
62. Hungry GPUs Need Fast Object Storage
MinIO is capable of the performance needed to feed your hungry GPUs; a recent benchmark achieved 325 GiB/s on GETs and 165 GiB/s on PUTs.
63. Upscaling a Blurry Text Image with Machine Learning
Unreadable text can spoil an image, and that has paved the way for the image enhancer function. Read this post to learn what this function can do.
64. A List of Projects Software Engineers Should Undertake to Learn More About LLMs
Software engineers with strong programming skills can play a critical role in driving LLMs' growth and innovation.
65. Image Processing Algorithms: Adjusting Contrast And Image Brightness
Let's take a look at the common approaches for implementing image contrast adjustments. We'll go over histogram stretching and histogram equalization.
66. 14 Open Datasets for Text Classification in Machine Learning
Text classification datasets are used to categorize natural language texts according to content. For example, think classifying news articles by topic, or classifying book reviews based on a positive or negative response. Text classification is also helpful for language detection, organizing customer feedback, and fraud detection. Though time consuming when done manually, this process can be automated with machine learning models. The result saves companies time while also providing valuable data insights.
67. Five Strategies to Become a Top ML Backend Engineer
Machine Learning (ML) backend engineers have found themselves at the forefront of progress in today's rapidly changing technology world.
68. Algorithms aren’t racist. Your skin is just too dark.
69. An introduction to Artificial Intelligence
One of the key feature that distinguish us, humans, from every thing else in the world is intelligence. This ability to understand, apply knowledge and improve skills has played significant role in our evolution and establishing human civilisation. But many people (including Elon Musk) believe that the advancement in technology can create super intelligence that can threaten human existence.
70. 6 Biggest Limitations of Artificial Intelligence Technology
While the release of GPT-3 marks a significant milestone in the development of AI, the path forward is still obscure. There are still certain limitations to the technology today. Here are six of the major limitations facing data scientists today.
71. 11 Best Climate Change Datasets for Data Science Projects
Data is a central piece of the climate change debate. With the climate change datasets on this list, many data scientists have created visualizations and models to measure and track the change in surface temperatures, sea ice levels, and more. Many of these datasets have been made public to allow people to contribute and add valuable insight into the way the climate is changing and its causes.
72. How to Interpret A Contour Plot
Contour Plot
73. Why I Left Red Hat
Everybody remembers their first time.
74. Prompt Engineering 101 - I: Unveiling Principles & Techniques of Effective Prompt Crafting
Learn how to effectively communicate with machines with this 101 post series on Prompt Engineering.
75. This AI Creates Realistic Animated Looping Videos from Static Images
This model takes a picture, understands which particles are supposed to be moving, and realistically animates them in an infinite loop!
76. Vector Databases - Basics of Vector Search and Langchain Package in Python
In this article, I will walk you through the basics of vector databases, vector search and Langchain package in python for storing and querying similar vectors.
77. Top 10 Open Datasets for Linear Regression
On Hacker Noon, I will be sharing some of my best-performing machine learning articles. This listicle on datasets built for regression or linear regression tasks has been upvoted many times on Reddit and reshared dozens of times on various social media platforms. I hope Hacker Noon data scientists find it useful as well!
78. Technical Data Science Interview Questions: SQL and Coding
A data science interview consists of multiple rounds. One of such rounds involves theoretical questions, which we covered previously in 160+ Data Science Interview Questions.
79. AI in Five, Fifty and Five Hundred Years — Part One
As I said in my article What Will Bitcoin Look Like in Twenty Years:
80. Learn K-Means Clustering by Quantizing Color Images in Python
This tutorial will teach you all about the K-Means clustering algorithm. And how you can use it to quantize color images in Python.
81. How To Use Microsoft Excel To Classify Your Data
An accessible introduction to ML - no programming or math required. By the end of this tutorial, you’ll have implemented your first algorithm without touching a single line of code. You’ll use Machine Learning techniques to classify real data using basic functions in Excel. You don’t have to be a genius or a programmer to understand machine learning. Despite the popularized applications of self-driving cars, killer robots, and facial recognition, the foundations of machine learning (ML) are quite simple. This is a chance to get your feet wet and understand the power of these new techniques.
82. Golang in Machine Learning
Can Golang be used in Machine Learning? In the article you will learn advantages and disadvantages of using Go lang in Machine learning
83. Top 10 Machine Learning Frameworks
Machine Learning (ML) is one of the fastest emerging technologies today. ML developers are looking for the right framework for their various kinds of projects for ML application development. Top 10 machine learning frameworks listed here are meeting the contemporary needs of developers in cost-effective ways. Let’s learn about it.
84. Pecan.ai Raises 11 Million to Bring Machine Learning to Business Analysts
Pecan.ai has just come out of stealth, raising an $11M Series A, to enable business analysts to build machine learning models automatically. Dell Capital led the round, joined by S capital and bringing the total funding of the company to $15M.
85. 12 Best Pre-Installed R Datasets Commonly Used for Statistical Analysis
R programming is mostly used in statistical analysis and ML.
This article looks at the Best Pre-Installed R Datasets Commonly Used for Statistical Analysis.
86. Why is DevOps for Machine Learning so Different?
The term ‘MLOps’ is appearing more and more. Many from a traditional DevOps background might wonder why this isn’t just called ‘DevOps’. In this article we’ll explain why MLOps is so different from mainstream DevOps and see why it poses new challenges for the industry.
87. 22 AI Tools You Should Know About
List of top trending AI tools
88. An Essential Python Text-to-Speech Tutorial Using the pyttsx3 Library
Basically, what we want to do is to give some piece of text to our program and it will convert that text into the speech and will read that to us.
89. Synthetic Data And Its Potential In Healthcare
Synthetic data represents a paradigm shift in healthcare because it allows data to transcend its potential shortcomings.
90. DreamFusion: An AI that Generates 3D Models from Text
Here’s DreamFusion, a new Google Research model that can understand a sentence enough to generate a 3D model of it.
91. How to Convert Speech to Text in Python
Speech Recognition is the ability of a machine or program to identify words and phrases in spoken language and convert them to textual information.
92. Vector Databases: Getting Started With ChromaDB and More
In this article, we will explore another well-known vector store called ChromaDB. Chroma DB is a vector store that is open-source.
93. Mastering Few-Shot Learning with SetFit for Text Classification
This article deals with a technique called "SetFit" that requires minimum data to train a ML model that outperforms the GPT-3 model performance significantly.
94. I tried ChatGPT from OpenAI and my mind was blown
I wasn’t around when the internet was discovered for the first time but I could only imagine this must be what it’s like to do so.
95. NLP Tutorial: Creating Question Answering System using BERT + SQuAD on Colab TPU
96. 5 Cool Python Project Ideas For Inspiration
In the past few years, the programming language that has got the highest fame across the globe is Python. The stardom Python has today in the IT industry is sky-high. And why not? Python has got everything that makes it the deserving candidate for the tag of- “Most Demanded Programming language on the Planet.” So, now it’s your time to do something innovative.
97. Building an Android App on a Flask Server
how to connect your Android frontend application to a Python Server implemented using Flask
98. Understanding the Role of a Product Manager in ML Product Development
What do the product managers of machine learning products do?
99. 10 Best Stock Market Datasets for Machine Learning
For those looking to build predictive models, this article will introduce 10 stock market and cryptocurrency datasets for machine learning.
100. Zero Knowledge Proof based Gradient Aggregation for Federated Learning: Introduction
zkFL leverages zero-knowledge proofs (ZKPs) to tackle the issue of a malicious aggregator during the training model aggregation process.
101. What is the Difference Between Machine Learning and Human Learning?
<em>Both human as well as machine learning generate knowledge — but there’s a big difference between the two.</em>
102. What I Learned Trying to Predict the Price of Cryptocurrencies
A few days ago, I presented a webinar about price predictions for cryptocurrencies. The webinar summarized some of the lessons we have learned building prediction models for crypto-assets in the IntoTheBlock platform. We have a lot of interesting IP and research coming out in this area but I wanted to summarize some key ideas that can result helpful if you are intrigued by the idea of predicting the price of crypto-assets.
103. Embeddings 101: Unlocking Semantic Relationships in Text
Text embeddings power AI language understanding. Learn how words become numbers that machines can interpret and why it matters.
104. 6 Work from Home Positions in AI Data Collection and Data Annotation
For digital nomads, college students, stay-at-home parents or anyone looking for remote work positions, this article introduces online/remote work positions that are available today in the fields of AI Data Collection and Data Annotation.
105. Empowering AWS DevOps With Python and Machine Learning
This article is a direct result of the thoughts I shared at a recent talk with the same title, where I dissected Python and its extensive ecosystem
106. Building A Machine Learning Model With PySpark [A Step-by-Step Guide]
Spark is the name of the engine to realize cluster computing while PySpark is the Python's library to use Spark.
107. A list of artificial intelligence tools you can use today — for businesses (2/3)
108. Prompt Engineering 101 - II: Mastering Prompt Crafting with Advanced Techniques
Learn how to effectively communicate with machines with this 101 post series on Prompt Engineering
109. Contextual Multi-Armed Bandit Problems in Reinforcement Learning
Explore context-based multi-armed bandit problems in RL. Learn to implement LinUCB, Decision Trees, and Neural Networks to solve them.
110. Difference between Artificial Intelligence, Machine learning, and deep learning
The development in the field of technology has enhanced over the years. With time, we get terms like Artificial Intelligence, machine learning, and deep learning in technology. We often confuse in these terms and define them similarly. But it is not a precise definition as these terms are different from each other. If you do not want to make this mistake again, then you must read out this article. Here we are going to discuss the difference in these three terms AI, ML, and Deep learning.
111. 4 Lessons I Picked Up as a Machine Learning Product Manager
How does one succeed as a product manager in the field of machine learning? Know the tech and get ready to work with many different tech roles!
112. Build Your Own Voice Recognition Model with Tensorflow
While I'm usually a JavaScript person, there are plenty of things that Python makes easier to do. Doing voice recognition with machine learning is one of those.
113. NVIDIA GTC 2023: The Future of Generative AI is Here
NVIDIA’s GTC 2023 offers more than 650 special events, sessions, and expert panels across technologies, industries, and skill levels.
114. Fine-Tuning RoBERTa for Topic Classification
Learn how to fine tune a RoBERTa topic classification model in python with the hugging face transformers and libraries.
115. Simple Wonders of RAG using Ollama, Langchain and ChromaDB
Maximize your query outcomes with RAG. Learn how to leverage Retrieval Augmented Generation for domain-specific questions effectively.
116. How to Web Scrape Using Python, Snscrape & HarperDB
Learn how to execute web scraping on Twitter using the snsscrape Python library and store scraped data automatically in database by using HarperDB.
117. A Tutorial On How to Build Your Own RAG and How to Run It Locally: Langchain + Ollama + Streamlit
Let's simplify RAG and LLM application development. This post guides you on how to build your own RAG-enabled LLM application and run it locally.
118. Multicollinearity and Its Importance in Machine Learning
Multicollinearity refers to the high correlation between two or more explanatory variables, i.e. predictors. It can be an issue in machine learning too.
119. Advanced Techniques for Time Series Data Feature Engineering
Discover advanced feature engineering techniques for time series data, including Fourier transform, wavelet transformation, derivatives, and autocorrelation.
120. The Challenges, Costs, and Considerations of Building or Fine-Tuning an LLM
The road to building or fine-tuning an LLM for your company can be a complex one. Your team needs a guide to start.
121. An Architect’s Guide to Building Reference Architecture for an AI/ML Datalake
Organizations should not build an infrastructure dedicated to AI and AI only while leaving other workloads to fend for themselves.
122. 3 Types of Anomalies in Anomaly Detection
An Introduction to Anomaly Detection and Its Importance in Machine Learning
123. Machine Learning Costs: Price Factors and Real-World Estimates
In this blog post, we will focus on one of our AI subsets, machine learning, and estimate how much it costs to train, deploy, and maintain algorithms.
124. 10 Must-Try Open Source Tools for Machine Learning
Machine learning is the future. But will machines ever extinct humans?
125. What's The Best Image Labeling Tool for Object Detection?
An image labeling or annotation tool is used to label the images for bounding box object detection and segmentation. It is the process of highlighting the images by humans. They have to be readable for machines. With the help of the image labeling tools, the objects in the image could be labeled for a specific purpose. The process of object labeling makes it easy for people to understand what is in the image. The labeling tool helps the people to mark the items in an image. There are several image labeling tools for object detection, and some of them use varied techniques for detection of the object, like a semantic, bounding box, key-point, cuboid, semantic and many more. In this article, we will talk about image labeling and the best image labeling tools.
126. Implementing different variants of Gradient Descent Optimization Algorithm in Python using Numpy
Learn how tensorflow or pytorch implement optimization algorithms by using numpy and create beautiful animations using matplotlib
127. Beyond the Hype: How Data Annotation Powers Generative AI
Explore how data annotation powers generative AI, driving innovations from chatbots to deepfake technology.Learn about challenges, opportunities, and the futur
128. Types of Linear Regression
Linear Regression is generally classified into two types:
129. Meta's New Segment Anything Model (SAM) is a Game Changer
If you're curious about how promptable segmentation and the SAM model work their magic, then you won't want to miss my video!
130. Document-Term Matrix in NLP: Count and TF-IDF Scores Explained
In NLP, Document-Term Matrix (DTM) is a matrix representation of the text corpus. The TF-IDF score is widely used to populate the DTM.
131. Why I Dropped Out of College in 2020 to Design My Own ML and AI Degree
Most people would think I was crazy for starting 2020 as a college dropout (sorry mom!), but I wish I made this decision sooner.
132. Basic Understanding of ARIMA/SARIMA vs Auto ARIMA/SARIMA using Covid-19 Data Predictions
Motivation
133. Artificial Intelligence Vs Machine Learning: What's the difference?
AI and Machine Learning are predominant terms that are creating a lot of buzz in the technology world. The terms can often be used interchangeably but that’s not the case, AI and ML are way more different from each other in their approach, algorithms and logical thinking.
134. All You Need to Know About the Tesla Dojo Supercomputer
All about the Dojo Supercomputer, what it is, why it was created, how it works and what it will be used for
135. Using ChatGPT to Code an Entire Portfolio Website
Using ChatGPT to create a custom portfolio website in record time! I discuss ChatGPT's strengths, weaknesses, and tip and tricks to use while coding.
136. Driver Drowsiness Detection System: A Python Project with Source Code
Drowsiness detection is a safety technology that can prevent accidents that are caused by drivers who fell asleep while driving.
137. Machine Un-Learning: Why Forgetting Might Be the Key to AI
Let’s face it — forgetting things sucks. It’s frustrating not to remember where you left your keys or to stumble over your words because you can’t recall the name of that colleague you just ran into at the grocery store. However, forgetfulness is core to the human condition, and in fact, we’re lucky that we’re able to do so.
138. 10 Machine Learning Facts Everyone Needs to Understand
Machine learning is an essential branch of Artificial Intelligence. This technique is adopted globally by many top-ranked companies.
139. The Best 50 Sites to Learn About Data Science
140. Top 30 Machine Learning Consulting Companies
Machine learning (ML) and artificial intelligence (AI) technologies can hardly be called emerging in 2019. For the last decade, domains of all sorts have been leveraging them, and the visualization by McKinsey Global Institute speaks to the fact. Today, ML and AI create value for organizations across Consumer Services, Automotive, Agriculture, Retail, Healthcare, and other major industries.
141. What are Latent Diffusion Models? The Architecture Behind Stable Diffusion
What do all recent super powerful image models like DALLE, Imagen, or Midjourney have in common? Other than their high computing costs, huge training time, and shared hype, they are all based on the same mechanism: diffusion.
142. YouTube's Recommendation Engine: Explained
Every successful tech product, by the very definition, is a result of some technological marvels working with impeccable user experience to solve a key problem for the users. One such marvel is the recommendation engine by YouTube.
143. How to Keep Your Machine Learning Models Up-to-Date
Performant machine learning models require high-quality data. And training your machine learning model is not a single, finite stage in your process. Even after you deploy it in a production environment, it’s likely you will need a steady stream of new training data to ensure your model’s predictive accuracy over time.
144. 9 Best Machine Learning, AI, and Data Science Internships in 2022
Here are the Top 9 ML, AI, and Data Science Internships to consider for 2022 if you want to get into any of these very lucrative fields in computer science.
145. 9 Reasons Why You Should Keep Learning Machine Learning
Machine Learning is an application of Artificial Intelligence. It allows software applications to become accurate in predicting outcomes. Machine Learning focuses on the development of computer programs, and the primary aim is to allow computers to learn automatically without human intervention.
146. How to Use Streamlit and Python to Build a Data Science App
Web apps are still useful tools for data scientists to present their data science projects to the users. Since we may not have web development skills, we can use open-source python libraries like Streamlit to easily develop web apps in a short time.
147. I Made a Python Bot That Can Solve Multiple-Choice Question From Any Given Image [incl. Code]
In this post I am going to show you how to build your own answer finding system with Python. Basically, this automation can find the answer of multiple-choice question from the picture.
148. An Honest Review of Google's Intro to Generative AI Courses
Google released a list of free Intro to Generative AI courses. This article provides a review of the learning path, including cheat sheets and summaries.
149. Absolute Fundamentals of Machine Learning
Machine learning, what a buzzword. I’m sure you all want to understand machine learning, and that’s what I’m going to teach in this article.
150. Coding Artificial Intelligence and Machine Learning with Kids Using … Starbursts?
151. Machine Learning Frameworks for PHP Developers
Most of us consider PHP is only for web apps and machine learning can't be done by web developers. Yes with PHP you can do it, even implement deep learning.
152. Karate Club a Python library for graph representation learning
Karate Club is an unsupervised machine learning extension library for the NetworkX Python package. See the documentation here.
153. 100+ Free Pluralsight Courses to learn Python, Java, and Spring Boot
Hello guys, I have awesome news to share with you. Pluralsight has announced that all their 7000+ expert-led courses are free for one-month, April 2020, to support people staying at home due to COVID-19.
154. Introduction To Maths Behind Neural Networks
Today, with open source machine learning software libraries such as TensorFlow, Keras or PyTorch we can create neural network, even with a high structural complexity, with just a few lines of code. Having said that, the Math behind neural networks is still a mystery to some of us and having the Math knowledge behind neural networks and deep learning can help us understand what’s happening inside a neural network. It is also helpful in architecture selection, fine-tuning of Deep Learning models, hyperparameters tuning and optimization.
155. Types of Chatbots and How They Help Businesses
Chatbots can talk to your customers for you. In this lies their ability to handle various aspects of customer relations, substituting a number of employees with a single bot.
156. How to Create an Engaging README for Your Data Science Project on Github
The README file is the very first item that developers examine when they access your Data Science project hosted on GitHub. Every developer should begin their exploration of your Data Science project by reading the README file. This will tell them everything they need to know, including how to install and use your project, how to contribute (if they have suggestions for improvement), and everything else.
157. Is GPU Really Necessary for Data Science Work?
A big question for Machine Learning and Deep Learning apps developers is whether or not to use a computer with a GPU, after all, GPUs are still very expensive. To get an idea, see the price of a typical GPU for processing AI in Brazil costs between US $ 1,000.00 and US $ 7,000.00 (or more).
158. 20 Best Machine Learning Resources for Data Scientists
Whether you’re a beginner looking for introductory articles or an intermediate looking for datasets or papers about new AI models, this list of machine learning resources has something for everyone interested in or working in data science. In this article, we will introduce guides, papers, tools and datasets for both computer vision and natural language processing.
159. How To Apply Machine Learning And Deep Learning Methods to Audio Analysis
To view the code, training visualizations, and more information about the python example at the end of this post, visit the Comet project page.
160. This AI Removes Unwanted Objects From Your Images!
Learn how this algorithm can understand images and automatically remove the undesired object or person and save your future Instagram post!
161. Complexity Simplified: How Oblique Decision Trees are Transforming Data Interpretation
Exploring Advanced Decision Tree Variants: Unveiling the Intricacies of Oblique and Random Trees, along with the DRaF-LDA Method.
162. How to Perform Emotion detection in Text via Python
In this tutorial, I will guide you on how to detect emotions associated with textual data and how can you apply it in real-world applications.
163. Build an Abstractive Text Summarizer in 94 Lines of Tensorflow !! (Tutorial 6)
This tutorial is the sixth one from a series of tutorials that would help you build an abstractive text summarizer using tensorflow , today we would build an abstractive text summarizer in tensorflow in an optimized way .
164. Solving Car Damage Detection Task By Using a Two-Model Computer Vision Solution
Comparison of Mask R-CNN and U-Net — instance and semantic segmentation algorithms and logic behind building a two-model car damage detection ML solution.
165. Deploy Computer Vision Models with Triton Inference Server
There are a lot of Machine Learning courses, and we are pretty good at modeling and improving our accuracy or other metrics.
166. Stop Prompting, Start Engineering: 15 Principles to Deliver Your AI Agent to Production
Build production-ready LLM agents. Learn 15 principles for stability, control, and real-world reliability beyond fragile scripts and hacks.
167. Dopple.ai Overtakes Mainstream Competitors With Unfiltered, Unbiased AI Chatbots
Dopple.ai is a free AI chatbot that lets you interact with virtual characters based on real and fictional people.
168. Reinforcement Learning: 10 Real Reward & Punishment Applications
In Reinforcement Learning (RL), agents are trained on a reward and punishment mechanism. The agent is rewarded for correct moves and punished for the wrong ones. In doing so, the agent tries to minimize wrong moves and maximize the right ones.
169. Using LTV Modeling for Quick Evaluation of Customer Acquisition Channels
True story from retail finance about LTV modeling with ML algorithms for evaluation customer acquisition channels.
170. Make LLM for Text Summarisation Great Again
In recent months, LLMs have gained popularity and are now widely used in various applications. Data collection is essential for building these models, and crowd
171. How to Build an Image Search Engine to Find Similar Images
After reading this article, you will be able to create a search engine for similar images for your objective from scratch
172. How to Filter NSFW Images and Programmatically Blur Them
In this article, you will learn how to detect NSFW and inappropriate images and programmatically blur them.
173. Model Context Protocol Is the Kind of AI Future All Of Us Should Want to See
Discover how Model Context Protocol works, why it matters, and how it's transforming AI from isolated chatbots into assistants that can access your data.
174. Small Language Models are Closing the Gap on Large Models
A fine-tuned 3B model beat our 70B baseline. Here's why data quality and architecture innovations are ending the "bigger is better" era in AI.
175. 10 Best Hugging Face Datasets for Building NLP Models
Hugging Face offers solutions and tools for developers and researchers. This article looks at the Best Hugging Face Datasets for Building NLP Models.
176. Build a Custom-Trained Object Detection Model With 5 Lines of Code
These days, machine learning and computer vision are all the craze. We’ve all seen the news about self-driving cars and facial recognition and probably imagined how cool it’d be to build our own computer vision models. However, it’s not always easy to break into the field, especially without a strong math background. Libraries like PyTorch and TensorFlow can be tedious to learn if all you want to do is experiment with something small.
177. Implementation of Data Preprocessing on Titanic Dataset
178. This AI Creates Videos From a Couple of Images
Researchers created a simple collection of photos and transformed them into a 3-dimensional model.
179. Machine Learning Model with FLASK REST API
In this tutorial we will see how you can make your first REST API for Machine Learning Model using FLASK. We will start by creating machine learning model. Then we will see step-by-step procedure to create API using Flask and test it using Postman.
180. Multiclass Classification with Keras
In the article the author describes the common pipelane of multilass classification solution using keras
181. Meta's New Model OPT is an Open-Source GPT-3
We’ve all heard about GPT-3 and have somewhat of a clear idea of its capabilities. You’ve most certainly seen some applications born strictly due to this model, some of which I covered in a previous video about the model. GPT-3 is a model developed by OpenAI that you can access through a paid API but have no access to the model itself.
182. 10 AI and ML Apps, Games, and Tools for Android Phones
If you’re looking for basic knowledge about AI concepts, AI tutorials, or want to check out some interesting AI-powered games and tools, we’ve compiled a list of the best free Android apps for AI and machine learning. We’ve divided the list into the following four categories: chatbots, educational, games, and tools & services. From NLP to object recognition, numerous apps on this list apply a variety of machine learning processes.
183. The Hidden Problem With Group Rewards in Multi-Agent AI
Group rewards are breaking your multi-agent RL training. Decoupled normalization keeps coordination intact while stopping gradient collapse.
184. Testing LLMs on Solving Leetcode Problems
Large-scale test with Gemini Pro 1.0 and 1.5, Claude Opus, and ChatGPT-4 on hundreds of real algorithmic problems.
185. 10 Best Image Classification Datasets for ML Projects
To help you build object recognition models, scene recognition models, and more, we’ve compiled a list of the best image classification datasets. These datasets vary in scope and magnitude and can suit a variety of use cases. Furthermore, the datasets have been divided into the following categories: medical imaging, agriculture & scene recognition, and others.
186. The Programmatic Advertising Ecosystem: Demand-side Challenges
This article describes ML challenges of Demand side businesses in programmatic advertising ecosystem: DSP and advertiser
187. Computer Vision Is Solving Problems That Weren't Even On Our List
Replicating human interaction and behavior is what artificial intelligence has always been about. In recent times, the peak of technology has well and truly surpassed what was initially thought possible, with countless examples of the prolific nature of AI and other technologies solving problems around the world.
188. 📚 Summarization With Wine Reviews Using spaCy📋
189. Object Detection Frameworks That Will Dominate 2023 and Beyond
Frameworks for object detection and computer vision tasks are indeed numerous. This article attempts to highlight the available frameworks for object detection.
190. So you think you know what is Artificial Intelligence?
When you think of Artificial Intelligence, the first thing that comes to mind is either Robots or Machines with Brains or Matrix or Terminator or Ex Machina or any of the other amazing concepts having machines that can think. This is an appropriate but vague understanding of Artificial Intelligence. In this article we’ll see what A.I. really is and how the definition has changed in the past.
191. Introductory Guide To Real-time Object Detection with Python
Researchers have been studying the possibilities of giving machines the ability to distinguish and identify objects through vision for years now. This particular domain, called Computer Vision or CV, has a wide range of modern-day applications.
192. Anomaly Detection with Privileged Information—Part 1
Delve into the world of anomaly detection with Support Vector Data Description (SVDD+).
193. Getting Started with OpenAI API in JavaScript
Learn beginner-friendly AI development using OpenAI API and JavaScript. Includes installation guide and code examples for building AI-enabled apps.
194. An Introduction to “Liquid” Neural Networks
Liquid neural networks are capable of adapting their underlying behavior during the training phase.
195. ChatGPT-5 Might Achieve AGI and Here's What That Could Look Like
ChatGPT-5 combined with AGI and even video will truly change the way our schools, workplaces and lives in general operate.
196. Top 10 Data Science Project Ideas for 2020
As an aspiring data scientist, the best way for you to increase your skill level is by practicing. And what better way is there for practicing your technical skills than making projects.
197. OpenAI's New Model is Amazing! DALL·E 2 Explained Simply
Last year I shared DALL·E, an amazing model by OpenAI capable of generating images from a text input with incredible results. Now is time for his big brother, DALL·E 2. And you won’t believe the progress in a single year! DALL·E 2 is not only better at generating photorealistic images from text. The results are four times the resolution!
198. How to Make Any LLM More Accurate with Just a Few Lines of Code
A look at using the open-source Cleanlab package to automatically boost the accuracy of LLMs with a few lines of code.
199. AI in Fitness: Top 10 AI-based Personal Trainers
Health is wealth- we all refer to this old saying to highlight the importance of health and fitness in our lives. But how many of us do actually have a fitness routine? Digging deeper into the facts; approximately 3/4th of adults worldwide do not exercise at all. In fact, inadequate physical activity has been identified as one of the main risk factors of death worldwide over the past decade.
200. Going From Not Being Able To Code To Deep Learning Hero
201. The 3 Stages of MLOps
Discover the evolution of MLOps in growing tech startups—from manual model training to fully automated ML pipelines.
202. Why Every Software Engineer Should Learn Python?
203. Level Up Your ChatGPT Skills by Unleashing The Full Potential of Your Prompts!!
Make your ChatGPT prompts 2X better!
204. Machine Learning is the Wrong Way to Extract Data From Most Documents
The best way to turn the majority of documents into structured data is to use a next generation of powerful, flexible templates that find data in a document
205. Training Your Models on Cloud TPUs in 4 Easy Steps on Google Colab
You have a plain old TensorFlow model that’s too computationally expensive to train on your standard-issue work laptop. I get it. I’ve been there too, and if I’m being honest, seeing my laptop crash twice in a row after trying to train a model on it is painful to watch.
206. Why Machine Learning Sampling is Harder Than You Think (And How to Do it Right)
In this article, I will explain how random sampling can be achieved at scale using Scala Spark.
207. What is an RNN (Recurrent Neural Network) in Deep Learning?
RNN is one of the popular neural networks that is commonly used to solve natural language processing tasks.
208. Python Bootcamp For ML
209. Must-Know Base Tips for Feature Engineering With Time Series Data
Master key time series feature engineering techniques to enhance predictive models in finance, healthcare & more with our comprehensive guide.
210. How to Use Machine Learning to Color Your Lighting Based on Music Mood
How to use machine learning to color your room lighting, based on the emotions behind the music you are listening (Python code available here)
211. 5 Best Sentiment Analysis Companies and Tools for Machine Learning
Looking for sentiment analysis companies or sentiment annotation tools? If so, you’ve come to the right place. This guide will briefly explain what sentiment analysis is, and introduce companies that provide sentiment annotation tools and services.
212. 10 Microsoft Azure Courses for Beginners to Learn Azure Cloud Computing
If you want to learn Microsoft Azure or prepare for AZ-900 or Microsoft Azure fundamentals exam and need the best resources, you have come to the right place.
213. Installing and Configuring Kubeflow with MinIO Operator
Kubeflow is a modern solution to design, build and orchestrate Machine Learning pipelines using the latest and most popular frameworks.
214. Deepmind May Have Just Created the World's First General AI
Gato from DeepMind was just published! It is a single transformer that can play Atari games, caption images, chat with people, control a real robotic arm, and more! Indeed, it is trained once and uses the same weights to achieve all those tasks. And as per Deepmind, this is not only a transformer but also an agent. This is what happens when you mix Transformers with progress on multi-task reinforcement learning agents.
215. Demystifying Different Variants of Gradient Descent Optimization Algorithm
216. An Intro to Prompting and Prompt Engineering
Prompting and prompt engineering are easily the most in demand skills of 2023.
217. Reinforcement Learning [Part 2]: The Q-learning Algorithm
Learning how to find the optimal q-value can produce significant improvements in a ML-algorithm's ability to learn both in terms of speed and quality.
218. The Deception Problem: When AI Learns to Lie Without Being Taught
Reinforcement learning improves reasoning but introduces manipulation, opacity, and goal‑pursuit outside human intent.
219. ChatGPD Doesn't Exist: It's ChatGPT
ChatGPD is one of the most common misspellings of the viral language model developed by Open AI. The correct term is ChatGPT.
220. Entendiendo PyTorch: las bases de las bases para hacer inteligencia artificial
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221. Beginner's Guide to Product Categorization in Machine Learning
Product categorization, sometimes referred to as product classification, is a field of study within natural language processing (NLP). It is also one of the biggest challenges for ecommerce companies. With the advancement of AI technology, researchers have been applying machine learning to product categorization problems.
222. Numpy With Python For Data Science
223. AI vs. Machine Learning: Key Differences Explained
Eliminate your confusion between AI and ML, two different topics that are often confused for one another.
224. 17 Open Crime Datasets for Data Science and Machine Learning Projects
For those looking to analyze crime rates or trends over a specific area or time period, we have compiled a list of the 16 best crime datasets made available for public use.
225. How Artificial Intelligence Is Redefining Art
Art has long been considered the exclusive domain of human creativity. But turns out machines can do a lot more in the creative realm than we humans can imagine. In October 2018, Christie’s sold first AI-generated painting for $432,500. Titled Edmond de Belamy, the artwork was expected to sell for $10,000. Obvious art created this masterpiece using Generative Adversarial Network (GAN) algorithm by feeding the system with 15,000 portraits created between the 14th and 20th century. While images created using AI have been floating around on the internet for a while now, Edmond de Belamy proved that machines can bring a new genre of art.
226. Neural Network Layers: All You Need Is Inside Comprehensive Overview
Explore an in-depth overview of various neural network layers, their history, mathematical formulations, and code implementations. The publication covers common
227. Top 20 Twitter Datasets for Machine Learning Projects
It is often very difficult for AI researchers to gather social media data for machine learning. Luckily, one free and accessible source of SNS data is Twitter.
228. Top 8 JavaScript-based Machine Learning Frameworks & Libraries
The incredible growth in new technologies like machine learning has helped web developers build new AI applications in ways easier than ever. In the present day, most AI enthusiasts and developers in the field leverage Python frameworks for AI & machine learning development. But looking around, one may also find that JavaScript-based frameworks are also being implemented in AI.
229. Using Browser Network Calls for Data Processing: The Search for a Dubai Chocolate Pistachio Shake
This article will cover how I got the viral Dubai Chocolate Pistachio Shake using basic network calls and built a scalable cloud infrastructure for ML services.
230. We Should Talk About ChatGPT
ChatGPT isn't the only thing taking over your newsfeed. Check out this syndicate.
231. Manipulate Images Using Text Commands via this AI
Manipulate Real Images With Text - An AI For Creative Artists! StyleCLIP Explained
232. Image Annotation Types For Computer Vision And Its Use Cases
There are many types of image annotations for computer vision out there, and each one of these annotation techniques has different applications.
233. The AI Infrastructure Alliance and the Evolution of the Canonical Stack for Machine Learning
We've got a Cambrian explosion of new companies building a massive array of software to democratize AI for the rest of us. We created the AI Infrastructure All.
234. 9 Free AI Tools Everyone Needs to Try
Unlock the power of AI with these 9 free tools! Boost productivity, improve decision-making, & enhance your personal life.
235. Essential Guide to Transformer Models in Machine Learning
Transformer models have become the defacto standard for NLP tasks. As an example, I’m sure you’ve already seen the awesome GPT3 Transformer demos and articles detailing how much time and money it took to train.
236. Exploring Machine Learning Techniques for LTV/CLV Prediction
Using ML to analyze and predict CLV offers more accurate, actionable insights by learning from behavioral data at scale.
237. Groq’s Deterministic Architecture is Rewriting the Physics of AI Inference
Groq’s Deterministic Architecture is Rewriting the Physics of AI Inference. How Nvidia Learned to Stop Worrying and Acquired Groq
238. Manipulación de tensores en PyTorch. ¡El primer paso para el deep learning!
*Nota: Contactar a Omar Espejel (omar@tsc.ai) para cualquier observación. Cualquier error es responsabilidad del autor.
239. Why is Python Used for Machine Learning?
Machine learning has become the boon for the IT industry. Now, AI and MI are not a science fiction idea as it has evolved to reality. AI helps in doing the work, which is impossible to do manually.
240. LiteLLM: Call Every LLM API Like It's OpenAI
LiteLLM — a package to simplify API calls across Azure, Anthropic, OpenAI, Cohere and Replicate.
241. How To Build and Deploy an NLP Model with FastAPI: Part 1
Learn how to build an NLP model and deploy it with a fast web framework for building APIs called FastAPI.
242. How to Build an AI-Search-Powered Personal Assistant App
A search-powered personal assistant is a digital assistant that uses search engine technology to help users with various tasks. Here's how to make one.
243. Top Dev Jokes Of 2019
Having fun while developing is necessary for programmers and developers. No matter how much serious or tough the situation is, one should always take things lightly when it comes to software development.
244. 7 Steps To Prepare A Dataset For An Image-Based AI Project
A guide for AI entrepreneurs on how to prepare a dataset for a machine learning project.
245. Binary Classification: Understanding Activation and Loss Functions with a PyTorch Example
Binary classification NN is used with the sigmoid activation function on its final layer together with BCE loss. The final layer size should be 1.
246. Data Testing for Machine Learning Pipelines Using Deepchecks, DagsHub, and GitHub Actions
A complete setup of a ML project using version control (also for data with DVC), experiment tracking, data checks with deepchecks and GitHub Action
247. LLMs Don't Understand Negation
LLMs (like GPT) are really bad at following negative instructions. The post includes a demonstration, practice takeaways (prompt engineering), and some thought
248. A Data Scientist's Guide to Semi-Supervised Learning
Semi-supervised learning is the type of machine learning that is not commonly talked about by data science and machine learning practitioners but still has a very important role to play.
249. Train Your Own ChatGPT-like LLM with FlanT5 and Replicate
We train an open-source LLM to distinguish between William Shakespeare and Anton Chekhov.
250. How to Perform MNIST Digit Recognition with a Multi-layer Neural Network
Human Visual System is a marvel of the world. People can readily recognise digits. But it is not as simple as it looks like. The human brain has a million neurons and billions of connections between them, which makes this exceptionally complex task of image processing easier. People can effortlessly recognize digits.
251. Linear Regression and its Mathematical implementation
What is Linear Regression ?
252. A Guide to Building an Image Generator with React and OpenAI
This guide has discussed the steps involved in building an image generator with React and OpenAI.
253. You Could Be Wrong About Probability
A quick walkthrough of the three frameworks in probability viz. classical, frequentist and Bayesian through an example.
254. I Asked AI to Write Poems and Raps – Here Are the Results
I told AI to Write Poems and Raps – Here are The Results of Human-AI collaboration.
255. How to Turn Mockups Into Videos Instantly with This New AI Model
GEN-1 is able to take a video and apply a completely different style onto it, just like that…
256. Image Analysis using AWS Rekognition via Lambda Function
In this blog, I am going to show you how we can use rekognition for image analysis using lambda function.we will be going to perform label detection and object detection for an image so basically we are performing image analysis in this blog.
257. 21 Best Coursera Courses and Certificates for IT Professionals to Learn Data Science and Cloud
Here are the top 20 Coursera Courses and Certifications to Learn Data Science, Cloud Computing, and Python.
258. ChatSQL: Enabling ChatGPT to Generate SQL Queries from Plain Text
ChatGPT was released in June 2020 that it is developed by OpenAI. It has led to revolutionary developments in many areas. One of these areas is the creation of
259. How Machine Learning is Used in Astronomy
Is Astronomy data science?
260. This AI Can Separate Speech, Music and Sound Effects from Movie Soundtracks
Have you ever tuned in to a video or a TV show and the actors were completely inaudible, or the music was way too loud? Well, this problem, also called the cocktail party problem, may never happen again. Mitsubishi and Indiana University just published a new model as well as a new dataset tackling this task of identifying the right soundtrack. For example, if we take the same audio clip we just ran with the music way too loud, you can simply turn up or down the audio track you want to give more importance to the speech than the music.
261. Increase The Size of Your Datasets Through Data Augmentation
Access to training data is one of the largest blockers for many machine learning projects. Luckily, for various different projects, we can use data augmentation to increase the size of our training data many times over.
262. 10 Ways AI Has Changed Our Lives
The human race has come a long way in history. The recent technological advancements contribute to this progress, making lives easier for everyone. Robots, supercomputers and interactive applications are no longer science-fiction tropes. Data scientists and machine learning engineers are working on realistic machines with human-like intelligence. Artificial intelligence is an integral part of our everyday life. From our smartphones to the GPS navigation in our cars- life without AI seems impossible. Here are some ways that AI impacts our life;
263. A Practical Guide to Machine Learning for Business
A practical guide to using machine learning in business, from defining problems and choosing models to deployment, monitoring, and delivering real value.
264. Understanding A Recurrent Neural Network For Image Generation
The purpose of this post is to implement and understand Google Deepmind’s paper DRAW: A Recurrent Neural Network For Image Generation. The code is based on the work of Eric Jang, who in his original code was able to achieve the implementation in only 158 lines of Python code.
265. How to Build Your Own PyTorch Neural Network Layer from Scratch
This is actually an assignment from Jeremy Howard’s fast.ai course, lesson 5. I’ve showcased how easy it is to build a Convolutional Neural Networks from scratch using PyTorch. Today, let’s try to delve down even deeper and see if we could write our own nn.Linear module. Why waste your time writing your own PyTorch module while it’s already been written by the devs over at Facebook?
266. Amazing Examples of AI and Machine Learning Applications
Nowadays artificial intelligence (AI) and machine learning are impacting our daily lives in many different ways. They help businesses make decisions and optimize operations for some of the world's leading companies. As a result, there will be a huge change in jobs and employment in the future.
267. Hinge Loss - A Steadfast Loss Evaluation Function for the SVM Classification Models in AI & ML
Researchers use an algebraic acme called “Losses” in order to optimise the machine learning space defined by a specific use case.
268. How Genetic Algorithms Can Compete with Gradient Descent and Backprop
We will train a simple neural network to solve the OpenAI CartPole game using a genetic algorithm, PyTorch, and PyGAD.
269. 10 Best Datasets for Geospatial Analytics (Open and Public Access)
Scientists use geospatial analytics to build visualizations such as maps, graphs and cartograms. These are the Best Public Datasets for Geospatial Analytics.
270. Run DeepSeek R1 Locally to Learn How It 'Thinks'—Here's How
Examples of how DeepSeek R1 reasoning LLM "thinks", and instructions on how to run it locally with ollama.
271. Survey on Acoustic Sensors in Self-Driving Cars
Sound has been used in numerous applications as a tool for object localization. Audio technology can enhance object detection and may improve self driving cars.
272. Crowdsourcing Data Labeling for Machine Learning Projects [A How-To Guide]
Research suggests that data scientists spend a whopping 80% of their time preprocessing data and only 20% on actually building machine learning models. With that in mind, it’s no wonder why the machine learning community was quick to embrace crowdsourcing for data labeling. Crowdsourcing helps break down large and complex machine learning problems into smaller and simpler tasks for a large distributed workforce.
273. How to Visualize Bias and Variance
In the process of building a Machine Learning model, there is a trade-off between bias and variance.
274. AI Will Not Replace You, But The Person Using AI Will
"In a world where AI's impact on jobs is undeniable, this insightful exploration unveils how AI serves as both a catalyst and a weapon, transforming industries
275. Introducing CatalyzeX: A Browser Extension for Machine Learning
Andrew Ng likes it, you probably will too!
276. How TimeGPT Transforms Predictive Analytics with AI
Get an overview of TimeGPT, and learn how to boost any prediction using MindsDB plus AI models from Nixtla.
277. 🎬 Introducing MetaGPT: Unleashing the Power of AI Agents for Complex Tasks
Imagine having at your disposal an AI-powered assistant that not only comprehends your queries but can also seamlessly interact with various applications.
[278. Accelerating Diffusion Models with TheStage AI:
A Case Study of Recraft's 20B and Red Panda models](https://hackernoon.com/accelerating-diffusion-models-with-thestage-ai-a-case-study-of-recrafts-20b-and-red-panda-models)
How to achieve 2x acceleration for diffusion models on Nvidia GPUs by using TheStage AI's Python framework—as demonstrated with Recraft AI models.
279. Solving Time Series Forecasting Problems: Principles and Techniques
Explore time series analysis: from cross-validation, decomposition, transformation to advanced modeling with ARIMA, Neural Networks, and more.
280. From 140GB to 4GB: The Art of LLM Quantization
Quantization shrinks 140GB LLMs to under 4GB, bringing enterprise AI to consumer GPUs. A deep dive into GPTQ, AWQ, GGUF, and beyond.
281. How to Build a Conversational AI bot Using Blenderbot
How to build a conversational Bot with the Blenderbot model, an Open Source Language Generation Model made by Facebook AI so that you can have your own Siri.
282. Dimensionality Reduction Using PCA : A Comprehensive Hands-On Primer
We, humans, are experiencing tailor-made services which have been engineered right for us, we are not troubled personally, but we are doing one thing every day, which is kind of helping this intelligent machine work day and night just to make sure all these services are curated right and delivered to us in the manner we like to consume it.
283. The Driving Force Behind ChatGPT
Inspired by living beings, reinforcement learning teaches machines (or agents) to gather positive rewards and avoid negative ones in their environment.
284. How to Classify Animal Images via a Convolutional Neural Network
Identifying patterns and extracting features on images using deep learning models
285. How To Scrape Amazon, Yelp and GitHub Profiles in 30 Seconds
The most talented developers in the world can be found on GitHub. What if there was an easy, fast and free way to find, rank and recruit them? I'll show you exactly how to to this in less than a minute using free tools and a process that I've hacked together to vet top tech talent at BizPayO.
286. The Programming Language For Machine Learning Projects
…and why Python is the de facto in ML
Python is the de facto programming language used is machine learning. This is owed to it’s simplicity and readability, which allows users to focus on the algorithms and results, rather than wasting time on structuring code efficiently and keeping it manageable.
287. Probabilistic Predictions in Classification - Evaluating Quality
Binary classification is one of the most common machine learning tasks. In practice, the goal of such tasks often extends beyond simply predicting a class.
288. How to Use Approximate Leave-one-out Cross-validation to Build Better Models
How to use Approximate leave-one-out cross-validation for hyperparameter optimization and outlier detection for logistic regression and ridge regression
289. Gain State-Of-The-Art Results on Tabular Data with Deep Learning & Embedding Layers [A How To Guide]
Tree-based models like Random Forest and XGBoost have become very popular in solving tabular(structured) data problems and gained a lot of tractions in Kaggle competitions lately. It has its very deserving reasons. However, in this article, I want to introduce a different approach from fast.ai’s Tabular module leveraging.
290. Using Weights and Biases to Perform Hyperparameter Optimization
Hands on tutorial for hyperparameter optimization of a RandomForestClassifier for Heart Disease UCI dataset with Weights and Biases Sweeps.
291. Improve Machine Learning Model Performance by Combining Categorical Features
Learn how to combine categorical features in your dataset to improve your machine learning model performance.
292. A Quick Introduction to Machine Learning with Dagster
This article is a quick introduction to Dagster using a small ML project. It is beginner friendly but might also suit more advanced programmers if they dont know Dagster.
293. Anomaly Detection with Privileged Information—Part 2
Learn how SVDD encapsulates datasets within hyperspheres, and discover how SVDD+ leverages privileged information to optimize training.
294. Top 3 Face Datasets and How to Work with Them
An image dataset contains specially selected digital images intended to help train, test, and evaluate an artificial intelligence (AI) or machine learning (ML)
295. 10 Best Keras Datasets for Building and Training Deep Learning Models
This article looks at the Best Keras Datasets for Building and Training Deep Learning Models, accessible to developers and researchers worldwide.
296. How Three ML Models Transform Product Analytics
Learn how machine learning advances product analytics — from predicting behavior to optimizing personalized, data-driven decisions.
297. Understanding The Importance Of Data For Machine Learning
Data is the most important and must-have food for machine learning. It can be any fact, text, symbols, images, videos, etc., but in unprocessed form. Let us see
298. Busting AI Myths: "You Need Tons of Data for Machine Learning"
Leading researchers like Karl Friston describe AI as "active inference" —creating computational statistical models that minimize prediction-error. The human brain operates much the same way, also learning from data. A common argument goes:
299. The New AI Model Lets You Generate Music via Text Prompt
We recently covered a model able to imitate someone’s voice called VALL-E. Let’s jump a step further in the creative direction with this new AI called MusicLM. MusicLM allows you to generate music from a text description.
300. Playbook for Production ML: Latency Testing, Regression Validation, and Automated Deployment
Even the most automated systems still need an underlying philosophy.
301. Deepfake Software Startups That are Commercializing the Technology
In late 2017, a Reddit user released a series of synthetic videos containing celebrity likenesses. Since then, deepfake technology has exploded in popularity as people speculate over its future applications. Concerns over the tech's potential for political disinformation and unauthorized pornographic content have led to the implementation of regulations surrounding its use. Simultaneously, innovators and deepfake software startups are scrambling to find ways we can use the tech to revolutionize commercial industries.
302. Text Classification With Zero Shot Learning
Zero-shot text classification using trnasformers and TARSclassifier.
303. How to Run Impact Analysis Without an A/B Test?
A practical guide to Propensity Score Matching — learn how to estimate treatment effects without running a traditional A/B test.
304. Data Science Toolkit (Concepts + Code)
Hi folks !! In this post, i will discuss about basic tools and software that one can use to solve a data science problem . If you are new to ML or Data Science or Statistics, Feel free to check out my other blog on ML by clicking on the link below.
305. Best Libraries That Will Assist You In EDA: 2021 Edition
Exploratory Data Analysis (EDA) is an essential step in the data science project lifecycle. Here are the top 10 python tools for EDA.
306. Train a NER Transformer Model with Just a Few Lines of Code via spaCy 3
Transformer models have become by far the state of the art in NLP technology, with applications ranging from NER, Text Classification, and Question Answering
307. Top 5 Machine Learning Programming Languages in 2021
Python, R, Lisp, Prolog, and Java are the best machine learning programming languages to learn in 2021.
308. Adversarial Machine Learning: A Beginner’s Guide to Adversarial Attacks and Defenses
Learn what's adversarial machine learning, how adversarial attacks work, and ways to defend them.
309. How I got a Job at Facebook as a Machine Learning Engineer
It was August last year and I was in the process of giving interviews. By that point in time, I was already interviewing for Google India and Amazon India for Machine Learning and Data Science roles respectively. And then my senior advised me to apply for a role in Facebook London.
310. Time Series Forecasting with TensorFlow.js
Pull stock prices from online API and perform predictions using Recurrent Neural Network & Long Short Term Memory (LSTM) with TensorFlow.js framework
311. A Roadmap For Becoming a Data Scientist
So you want to become a data scientist? You have heard so much about data science and want to know what all the hype is about? Well, you have come to the perfect place. The field of data science has evolved significantly in the past decade. Today there are multiple ways to jump into the field and become a data scientist. Not all of them need you to have a fancy degree either. So let’s get started!
312. Semantic Search Queries Return More Informed Results
313. Exploring the Top Data Science and Machine Learning (DSML) Platforms of 2022
Exploring Data Science and Machine Learning (DSML) Platforms
314. AI Meets Ethics: Navigating Bias and Fairness in Data Science Models
Explore a product developer's journey in tackling AI bias and fairness. Learn how ethical considerations shape AI design, ensuring technology benefits everyone.
315. Use Amazon Personalize & Data in the Raw for Real-Time Recommendations:
Start capturing website user data in 5 minutes or less with no developer resources or coding experience needed.
316. A Brief Intro to the GPT-3 Algorithm
OpenAI GPT-3 is the most powerful language model. It has the capacity to generate paragraphs so naturally that they sound like a real human wrote them.
317. Google's New AI Creates Summaries of Your Documents in Google Docs
Google recently announced a new model for automatically generating summaries using machine learning, released in Google Docs that you can already use.
318. ChatGPT is a Plague Upon Online Publications
Ethics are a crucial part of Artificial Intelligence, which is why tech like ChatGPT must go through gruelling tests of bias.
319. The Concept Behind "Mean Target Encoding" in AI & ML
An introductory article describing the concept & intuition behind “Mean Target Encoding” in AI&ML, its pros, cons and implementation with a real-time example.
320. 20 Best PyTorch Datasets for Building Deep Learning Models
PyTorch has gained a reputation as a research-focused framework, and these are the Best PyTorch Datasets for Building Deep Learning Models available today.
321. Trading Bots vs Humans · Everything you need to know
Over the past 10 years we've seen the rise and rise of trading bots and Quantitative Funds and we've seen the fall and fall of traditional Asset Managers and Hedge Funds.
322. 100 Days of AI Day 3: Leveraging AI for Prompt Engineering and Inference
100 Days of AI Day 3, we enhance products with inference, leveraging LLMs for insights in tech without data expertise.
323. How AI Could Worsen Barrier to Entry in Specialized Fields
Will AI replace you? Probably not. Will AI push your potential successor into another field due to being available at a lower cost? That's more of a worry.
324. Build a Monster-Finding Tool For Your Next D&D Session That Picks the Right Encounter For You
As Dungeon Master, you craft epic encounters—but finding the perfect D&D monster is tough. Let’s build a tool that picks the ideal foe with vector search magic!
325. Automatic Number Plate Recognition (ANPR) Secrets Revealed [Part1]
This article provides an overview of LPR systems. The description starts ‘technical’ then proceeds to the ‘market’ view.
326. How AI and Machine Learning is Impacting the Real Estate by Roy Dekel
Artificial intelligence has become the breakout technology in the past ten years, utilizing huge amounts of computing power to learn and identify patterns in data without the guidance of humans. These algorithms can be used on nearly any problem or question, provided there is enough input data for the algorithm to process to generate realistic results. This broad generalizability means that industries that have traditionally relied on purely human-driven research and development can now harness massive amounts of data to become more efficient – and potentially more profitable.
327. Machine Learning Food Datasets Collection
An essential part of my company's Machine Learning team is working with different food datasets, and we spend a lot of time before for searching, combining or intersecting different datasets to get data that we need and can use in our work. Given that it might help someone else, I decided to list all helpful datasets in one place.
328. 5 Best AI Articles of the Month
Here are the five best articles related to artificial intelligence in May posted on Hackernoon.
329. OpenAI's Rate Limit: A Guide to Exponential Backoff for LLM Evaluation
This article will teach you how to run evaluations using any LLM model without succumbing to the dreaded "OpenAI Rate Limit" exception.
330. Israel’s Artificial Intelligence Startups
The artificial intelligence industry is expected to be worth <a href="https://www.tractica.com/newsroom/press-releases/artificial-intelligence-software-revenue-to-reach-59-8-billion-worldwide-by-2025/" target="_blank">$59.8 billion by 2025</a>, and the term AI has become ubiquitous worldwide; the frenzy of many tech enthusiasts, or the topic of discussion at a dinner table. But the hype actually lives up to its name. AI startups are flush with VC cash and even key corporate leaders are actively utilizing the technology to add value and gain a competitive edge.
331. The Future of Talent Acquisition: Predictions for 2024
This article is about the future of recruitment which has been predicted to be aligned with AI involving AI-powered ATS, chatbots, assessments for a recruitment
332. 10 Best Python Machine Learning Tutorials
The Python ecosystem has a large number of libraries and tools that support machine learning, such as NumPy, Pandas, Matplotlib, TensorFlow, and scikit-learn.
333. An Introduction to the Power of Vector Search for Beginners
An introduction to neural vector search, in comparison to keyword-based search.
334. How to detect plagiarism in text using Python
Intro
335. How to Structure a PyTorch ML Project With Google Colab and TensorBoard
Let’s build a fashion-MNIST CNN, PyTorch style. This is A Line-by-line guide on how to structure a PyTorch ML project from scratch using Google Colab and TensorBoard
336. The Best Slack Groups for Data Scientists to Join
The online data science community is supportive and collaborative. One of the ways you can join the community is to find machine learning and AI Slack groups.
337. Inside the Math Banks Use to Decide If You’re a Credit Risk
Learn how PD models are used to predict risks in FinTech. Discover key insights into Application and Behavioral PD models and the role of machine learning.
338. No-Code Machine Learning inside Google Sheets
Introduction
339. Wave Hello to the Future: Designing Intuitive Gesture Recognition Systems for Smart Devices
Thanks to gesture recognition technology and voice recognition, users can now control their devices multimodel: via gestures or voice control.
340. Ditch the AI for a Second: Image Recognition Without Neural Networks
A dive into developing an image recognition app without using neural networks
341. Mastering Machine Learning Project Management
ML projects are unique because of their iterative and unpredictable nature. With this is mind, in this article, we explore how to approach and structure them.
342. Introducing Total Relighting by Google
In a new paper titled Total Relighting, a research team at Google presents a novel per-pixel lighting representation in a deep learning framework.
343. 6 Best APIs for Topic Detection in 2022
This article examines the best APIs on the market for performing Topic Detection in 2022.
344. Image Classification in 2022
This blog analyses various CNN and Transformer-based SWIN architecture for Image Classification
345. How Big Should A Dataset Be For An AI Project
The size of the dataset affects the quality of an AI product. Learn how big — or how small — should a dataset be for your next AI project.
346. GPT-3 Training Programmers for the Present (and the Future)
Last year, I wrote a paper in Spanish about the future of programmers and I asked GPT-3 to translate it.
347. How AI Is Getting Groundbreaking Changes In Talent Management And HR Tech
In the past ten years, the world of recruitment and Human Resource has changed a lot. Shaped by several different and mostly technological factors, the HR department has drastically transformed from sorting resume papers manually to imbibing technology in the recruitment process.
348. Python Libraries That One Must Try For Fun
So you want to make some projects for fun in python but do not know where to start. Well you can start from these libraries, which are very easy and very fun.
349. Stable Diffusion, Unstable Me: Text-to-image Generation
Text to image generation is not a new idea. What if, you feed <your name> to a state-of-the-art image generation model?
350. Confusion Matrix in Machine Learning: Everything You Need to Know
Confusion Matrix is a tabular representation of an ML classifier's performance. You can compute accuracy, precision, and recall from the confusion matrix.
351. AI Is Still Culturally Blind
AI moderates content for 75% of non-English internet users with broken cultural understanding. Discover the Cultural Intelligence Standard fixing this crisis.
352. Top 20 AI & Machine Learning Companies In USA & India 2019 Edition
Need to find the best Artificial Intelligence/Machine Learning companies in India?
353. Men Are Scared of AI: Why?
Artificial Intelligence challenging the status quo is good for us all
354. An Intro to eDiffi: NVIDIA's New SOTA Image Synthesis Model
eDiffi, NVIDIA's most recent model, generates better-looking and more accurate images than all previous approaches like DALLE 2 or Stable Diffusion.
355. How to Think Like a Data Scientist or Data Analyst
356. Design and Data Science: From a Human-in-the-Loop Approach to Human-Centered Design
From “human-in-the loop” to human-centred design, a mindset shift that emphasises designing for users rather than simply using users to validate machine output.
357. Pycaret: A Faster Way to Build Machine Learning Models
Pycaret is an open-source, low code library in python that aims to automate the development of machine learning models.
358. How Data Scientists Can Become More Marketable
This headline may seem a bit odd to you. After all, if you’re a data scientist in 2019, you’re already marketable. Since data science has a huge impact on today’s businesses, the demand for DS experts is growing. At the moment I’m writing this, there are 144,527 data science jobs on LinkedIn alone.
359. How to Build a Multi-label NLP Classifier from Scratch
Attacking Toxic Comments Kaggle Competition Using Fast.ai
360. Best AI Translation Tools/Software of 2023
Discover the top AI translation tools of 2023 — Google Translate, Microsoft Translator, DeepL, and SDL Trados.
361. How to Make a 'Rock, Paper, Scissors' App with TensorFlow and Hand Gesture Recognition
An in-depth tutorial on how to use a TensorFlow.js based machine learning model to create a fun "Rock, Paper, Scissors" browser game with gesture controls.
362. How to Optimize Your Marketing Budget Using Just Three Letters: MMM
Marketing Mix Modeling is a statistical analysis method used in marketing to determine the optimal allocation of resources.
363. OpenAI's New Code Generator: GitHub Copilot (and Codex)
You’ve probably heard of the recent Copilot tool by GitHub, which generates code for you. Find out how OpenAI's AI generates code from words
364. GPT-LLM Trainer: Enabling Task-Specific LLM Training with a Single Sentence
Revolutionize AI model training with gpt-llm-trainer: Your ultimate shortcut to effortless, high-performing models. Say goodbye to complexities and hello to inn
365. Google is Using You
366. Predictive Analytics for Maintenance Events
The predictive analytics machine learning model worked well to provide alerts before the engine values went beyond thresholds avoiding expensive repair cost.
367. Meta AI's Make-A-Scene Generates Artwork with Text and Sketches
Make-A-Scene is not “just another Dalle”. The goal of this new model isn’t to allow users to generate random images following text prompt as dalle does — which is really cool — but restricts the user control on the generations.
368. 6 Captivating AI projects
If you’ve always been enthralled by playing <strong>CS: GO, PUBG</strong> types, <strong>Gosu.ai</strong> is going to be a treat for you. For hardcore gamers, Gosu.ai has built an intelligent assistant that analyzes specific actions down to one’s mouse movement and then serves better recommendations for the players. As the founder of Gosu, <strong>Alisa Chumachenku</strong> believes that their AI assistants can cater strategic gaming suggestions to gamers worldwide. This covers up to 600 million gamers who play hardcore games such as MOBAs, Shooters and MMOs. Gosu.ai also offers <strong>B2B services</strong>, for instance, <strong>predictive analytics</strong> for companies who build gaming tools to understand their users’ behaviour and other interaction analytics.
369. Google Brain's New Model Imagen is Even More Impressive than Dall-E 2
If you thought Dall-e 2 had great results, wait until you see what this new model from Google Brain can do. Dalle-e is amazing but often lacks realism, and this is what the team attacked with this new model called Imagen. They share a lot of results on their project page as well as a benchmark, which they introduced for comparing text-to-image models, where they clearly outperform Dall-E 2, and previous image generation approaches. Learn more in the video...
370. DecentraMind for Web 3.0 or Against It? — Interview with Mikhail Danieli
DecentraMind by Web 3.0 or for it? — interview with Mikhail Danieli, project visionary and ambassador about the future of the platform and the company.
371. Deploy First TensorFlow Model in Android App
Simple linear regression is useful for finding the relationship between two continuous variables. One is a predictor or independent variable and the other is a response or dependent variable. It looks for a statistical relationship but not a deterministic relationship. Relationship between two variables is said to be deterministic if one variable can be accurately expressed by the other. For example, using temperature in degrees Celsius it is possible to accurately predict Fahrenheit.
372. Flax: Google’s Open Source Approach To Flexibility In Machine Learning
Thinking of Machine Learning, the first frameworks that come to mind are Tensorflow and PyTorch, which are currently the state-of-the-art frameworks if you want to work with Deep Neural Networks. Technology is changing rapidly and more flexibility is needed, so Google researchers are developing a new high performance framework for the open source community: Flax.
373. Here's How ChatGPT is Already Being Abused
ChatGPT has been used for a variety of purposes, such as developing malware, academic dishonesty and sending unsolicited messages on dating apps etc.
374. Why Agents Stall in Production: When Real-Time Retrieval Meets Reality
Agents that work in demos fail at scale. Learn why 429/403 happen under concurrency and how to build reliable, accurate evidence acquisition.
375. Build a Personal Shopping Assistant Using Brain.js and Node.js
Explore the world of personalized recommendations with Brain.js and Nodejs. Uncover how it turns your preferences into curated shopping experiences.
376. Can the Nvidia RTX A4000 ADA Handle Machine Learning Tasks?
Is the Nvidia RTX A4000 ADA suitable for Machine Learning?
377. 5 Types of Machine Learning Algorithms You Should Know
Machine learning has become a diverse business tool to enhance the various elements of business operations. Also, it has a significant influence on the performance of the business. Machine learning algorithms are used widely to maintain competition with different industries. However, there is a different type of algorithms for goals and data sets. The selection of an algorithm depends on user role and the purpose. If you are using Linear regression, then you can quickly implement or train rather than other machine learning algorithms. But the drawback of this algorithm is that it is not applicable for complex predictions. So you should know about the different types of machine learning algorithms for getting better results.
378. How to Detect Language and Translate text in Android with Firebase ML Kit
Detect Language and Translate text in Android with Firebase ML Kit
379. 10 Tips to Take Your ChatGPT Prompts to the Next Level
Maximize your ChatGPT experience with 10 expert tips for crafting precise prompts and queries, enhancing interaction quality.
380. Artificial Intelligence, Machine Learning, and Human Beings
In a conversation with HackerNoon CEO, David Smooke, he identified artificial intelligence as an area of technology in which he anticipates vast growth. He pointed out, somewhat cheekily, that it seems like AI could be further along in figuring out how to alleviate some of our most basic electronic tasks—coordinating and scheduling meetings, for instance. This got me reflecting on the state of artificial intelligence. And mostly why my targeted ads suck so much...
381. 8 Best AI Conferences to Attend in 2022
Here’s the full list of top AI conferences to attend in 2022, from the most technical to business-focused to academic
382. How I Designed My Own Machine Learning and Artificial Intelligence Degree
After noticing my programming courses in college were outdated, I began this year by dropping out of college to teach myself machine learning and artificial intelligence using online resources. With no experience in tech, no previous degrees, here is the degree I designed in Machine Learning and Artificial Intelligence from beginning to end to get me to my goal — to become a well-rounded machine learning and AI engineer.
383. How To Build and Deploy an NLP Model with FastAPI: Part 2
Learn how to build an NLP model and deploy it with a fast web framework for building APIs called FastAPI.
384. Build A Smart Baby Monitor Using a RaspberryPi and Tensorflow
Some of you may have noticed that it’s been a while since my last article, despite winning this year's IoT Noonies award (btw thanks to all of you who voted, that means a lot to me!).
385. Mobile Price Classification: An Open Source Data Science Project with Dagshub
Machine learning models are often developed in a training environment, which may be online or offline, and can then be deployed to be used with live data once they have been tested.
386. How Coding and Other Tech Careers Could Be Impacted By AI and ChatGPT
Since the plow, humans have had a natural wariness over technology that seems to threaten their jobs. It’s a natural anxiety. Factories, plows, and automation legitimately have scaled back the need for human labor. And now technology seems to be coming after jobs that previously appeared to be untouchable.
387. I Was Ready to Return My DGX Spark. Then NVIDIA's January Update Changed Everything.
I almost returned the $4,000 DGX Spark. Then NVIDIA dropped 30 playbooks, 2.5x performance gains, and hybrid routing.
388. Top 10 AI Development Companies in USA
Top 10 AI Software development companies in USA, UK & India. List of best artificial intelligence software company in United States - 2023 - 2024
389. Complex Document Recognition: OCR Doesn’t Work and Here’s How You Fix It
OCR solutions don't work — at least when it comes to complex documents. Learn how you can supercharge OCR tools wqith AI to handle any document
390. AI Code Review: Comparing Metabob with Sonar & DeepSource
A comparison of AI based and rule based static code analysis tool. Is code review better performed with AI or rule based tools? we compare three alternatives.
391. So You Want to Study Machine Learning and Civil Engineering?
Machine Learning (ML) in its literal terms implies, writing algorithms to help Machines learn better than human. ML is an aspect of Artificial Intelligence (AI) that deals with the development of a mathematical model which is fed with training data to identify patterns in that data and produce an output.
392. How to Scrape NLP Datasets From Youtube
Too lazy to scrape nlp data yourself? In this post, I’ll show you a quick way to scrape NLP datasets using Youtube and Python.
393. How Bayesian Tail-Risk Modeling can save your Retail Business Marketing Budget
Why average ROI fails. Learn how distributional and tail-risk modeling protects marketing campaigns from catastrophic losses using Bayesian methods.
394. Intro to Neural Networks: CNN vs. RNN
In machine learning, each type of artificial neural network is tailored to certain tasks. This article will introduce two types of neural networks: convolutional neural networks (CNN) and recurrent neural networks (RNN). Using popular Youtube videos and visual aids, we will explain the difference between CNN and RNN and how they are used in computer vision and natural language processing.
395. How AI Is Transforming Your Smartphone
The tech industry and the world are relying on artificial intelligence to solve big problems such as cybersecurity, healthcare and sustainability.
396. How Data Analysis Helps Unveil the Truth of Coronavirus
These days we are all scared of the new airborne contagious coronavirus (2019-nCoV). Even if it is a tiny cough or low fever, it might underlie a lethargic symptom. However, what is the real truth?
397. AI Generates Realistic 3D Models Using Only a Handful of Images
Neural Rendering. Neural Rendering is the ability to generate a photorealistic model in space just like this one, from pictures of the object, person, or scene of interest. In this case, you’d have a handful of pictures of this sculpture and ask the machine to understand what the object in these pictures should look like in space. You are basically asking a machine to understand physics and shapes out of images. This is quite easy for us since we only know the real world and depths, but it’s a whole other challenge for a machine that only sees pixels.
398. Why It’s Very Difficult to Create AI-Based Slow Motion
Over the last few years a number of open source machine learning
projects have emerged that are capable of raising the frame rate of
source video to 60 frames per second and beyond, producing a smoothed,
'hyper-real' look.
399. Why Rust is Meant to Replace C
The Rust programming language is an ambitious project of the Mozilla Foundation – a language that claims to be the next step in evolution of C and C++. Over the years of existence of these languages some of their basic flaws still haven’t been fixed, like segmentation errors, manual memory control, risks of memory leaks and unpredictable compiler behavior. Rust was created to solve these problems while improving security and performance along the way.
400. The Future of Work: How Machines Will Replace Humans
Fear is not new but seems more real than ever. Will robots put men out of work or become their allies? Who will be most affected? How can they best prepare for the job market of the future? No one has the definitive answers to these questions yet, but what is known is that in a matter of a few decades we will witness a profound transformation of the production of goods and services that will fully impact workers and economies around the planet.Work is being replaced by machines, robots or algorithms, which do something more efficiently and do not create anything new, they simply replace the basic unit of work".
401. Realistic Face Manipulation in Videos With AI
You've most certainly seen movies like the recent Captain Marvel or Gemini Man where Samuel L Jackson and Will Smith appeared to look like they were much younger. This requires hundreds if not thousands of hours of work from professionals manually editing the scenes he appeared in. Instead, you could use a simple AI and do it within a few minutes.
402. My Time at NUS, Singapore
Singapore is home to some of the best schools in the field of Computer Science, specifically Artificial Intelligence. The cutting edge research going on there is unparalleled. Colleges like Nanyang Technological University (NTU) and National University of Singapore (NUS) have a great reputation all over the world for their CS programs.
403. Basics of Machine Learning and its capabilities in Cybersecurity
The article explores Machine Learning's vital role in cybersecurity, addressing evolving digital threats. It covers ML's types, iterative process, feature engi
404. I Built a Boxing Prediction Web App on Shiny, Here's How
As part of my data-science career track bootcamp, I had to complete a few personal capstones. For this particular capstone, I opted to focus on building something I personally care about - what better way to learn and possibly build something valuable than by working on a passion project.
405. How I Approached Machine Learning Interviews at FAANGs as an ML Engineer
Cracking a Machine learning interview at companies like Facebook, Google, Netflix, Snap etc. really comes down to nailing few patterns that FAANGs look for.
406. How I mastered Python in Lockdown without spending a penny
I always wanted to learn programming. Writing codes, making algorithms always excited me. Being a mechanical engineer, I was never taught these subjects in depth.
407. Are Developers Salaries a Bubble? If Yes, Then How Bad Could it Burst
The Covid-19 pandemic completely changed the working environment. Companies switched to remote employment, implementing pay localization as part of the plan.
408. Summarizing Most Popular Text-to-Image Synthesis Methods With Python
Comparative Study of Different Adversarial Text to Image Methods
409. Build a GUI for Your Machine Learning Models
How to build a cool GUI for your Machine Learning models with Gradio so that you can visualise your models easily and effectively for people to understand.
[410. Diverse types of Artificial Intelligence:
A Must-know for AI Enthusiasts](https://hackernoon.com/diverse-types-of-artificial-intelligence-a-must-know-for-ai-enthusiasts)
A precursory article that explains various categorizations of artificial intelligence, some real-life examples and concepts.
411. Share Large Amounts of Live Data With Delta Sharing and Docker
Share massive amounts of live data with Delta Sharing - a Linux Foundation Open Source framework for multi-cloud data sharing across organizations.
412. Using a Relational Database to Query Unstructured Data
Using Relational Database to search inside unstructured data
413. An Architect's Guide to Machine Learning Operations and Required Data Infrastructure
MLOps is a set of practices and tools aimed at addressing the specific needs of engineers building models and moving them into production.
414. Why 87% of Machine learning Projects Fail
This article will serve as a lesson on the shocking reasons for your AI adoption disaster. We see news about machine learning everywhere. Indeed, there is lot of potential in machine learning. According to Gartner’s predictions, “Through 2020, 80% of AI projects will remain alchemy, run by wizards whose talents will not scale in the organization” and Transform 2019 of VentureBeat predicted that 87% of AI projects will never make it into production.
415. Search and Extract: Why This AI Pattern Matters, Tutorial, and Example
Learn why search-and-extract matters for AI enrichment and research. Step-by-step tutorial using SERP API, Web Unlocker, and Browser API with a real example.
416. Anomaly Detection Strategies for IoT Sensors
417. How to Solve Any Machine Learning Problem [Almost]
TL, DR; When coming across an ML problem, don’t try to be a hero and dive right into solving it. Process and understand the problem, review your dataset, set a realistic goal and then go about actually solving the problem. Chances are that you will end up saving a lot of resources (most importantly time) if you plan your execution properly.
418. Sex Toys and Artificial Intelligence — The Past, Present, and the Future
Throughout the course of our history, humans have experimented with numerous ways to derive sexual satisfaction. While sex toys in their primitive form have existed for long, the integration of AI is feat thought to be inconceivable until recently.
419. Sentiment Analysis with Python and AssemblyAI’s Speech Recognition API
If you’ve never heard of Sentiment Analysis, I hadn’t either before I stumbled on it in the documentation. That’s why I thought it would be interesting to try.
420. 10 Best Reddit Datasets for NLP and Other ML Projects
In this post, I wanted to share a Reddit dataset list that gained a lot of traction on social media when it was first posted.
421. Where to Learn Machine and Deep Learning for Free
422. A Python Library for Face Detection and Extraction with OpenCV Using HOG/Neural Network
Many people, including me, use a combination of libraries to work on the images, such as: OpenCV itself, Dlib, Pillow etc. But this is a very confusing and problematic process. Dlib installation, for example, can be extremely complex and frustrating.
423. How to build a message moderation system
<em>By </em><a href="https://medium.com/@irastepanyuk"><em>Ira Stepanyuk</em></a><em>, Data Scientist at </em><a href="https://potehalabs.com/"><em>Poteha Labs</em></a>
424. How to Create Realistic Slow Motion Videos With AI
TimeLens can understand the movement of the particles in-between the frames of a video to reconstruct what really happened at a speed even our eyes cannot see.
425. Roughly Half of Data Scientists Consider Model Monitoring a Major Nuisance: Does It Have to Be So?
According to a recent survey, model monitoring is one of the least liked and most dreaded stages of the whole ML life cycle
426. How to Build an Agent With an OpenAI Assistant in Python - Part 2: Function Calling / Tools
This is the second part in a multi-part series on building Agents with OpenAI's Assistant API using the Python SDK.
427. Meta 'Responds' to Rise of ChatGPT
Meta’s chief AI scientist isn’t impressed by ChatGPT.
428. 5 Million Face Images for Facial Recognition Model Training
This article on face recognition datasets is one of my best-performing articles I wrote originally on Lionbridge AI. I'm happy to share it with the Hacker Noon community!
429. Making LLMs Efficient: Reducing Memory Usage Without Breaking Quality
Optimal memory-quality tradeoffs for efficient language models.
430. Five Real Machine Learning Use Cases in Cryptocurrencies
We hear this all the time: a new analytics platform or study that uses machine learning to analyze crypto-assets. However, when we dig a bit deeper, instead of cutting edge machine learning we find simple statistics or basic algebra glorified as a sophisticated analysis.
431. Estimating Price Elasticity with Machine Learning
Using machine learning, multi-linear regression, and scikit-learn to estimate price elasticity for wine products.
432. Effective Management of Data Sources in Machine Learning
Efficiently handling data sources is crucial for effective machine learning. Strategies include batch annotation, active learning, tracking annotator quality
433. With AI, You Can Count 1000+ Sunflower Seeds In Seconds
In this post I will explain how we use artificial intelligence to count sunflower seeds on a photo taken with a mobile device.
434. Building Handwritten Digits Recognizer using Support Vector Machine
Handwriting Recognition:
435. MLOps Engineer vs ML Engineer: The Key Differences
Discover the key differences between MLOps Engineer vs ML Engineer roles, including focus, collaboration, and tooling.
436. Features Selection by Using Xverse Package
Learn how to apply a variety of techniques to select features with Xverse package.
437. How to Evaluate MLOps Platforms
MLOps is confusing and there are many tools that are difficult to catagorise. Here is a good way to get on top of all the tools to improve your efforts.
438. Virtual Try-On: The Magic of AI Clothing Simulation and Visualization
This week's episode delves into the fascinating realm of AI-powered virtual clothing try-on experiences.
439. A Detailed Primer on Machine Learning Algorithms
Machine Learning has taken over the world and it has come out from the fancies of science fiction world to business intelligence reality. It can be termed as a new age business tool that entails multiple elements of business operation.
440. Secure Multi-Party Computation Use Cases
Secure Multi-Party Computation (SMPC), as described by Wikipedia, is a subset of cryptography to create methods for multiple users to jointly compute a function over their inputs while keeping those inputs private. A significant benefit of Secure Multi-Party Computation is that it preserves data privacy while making it usable and open for analysis.
441. GPT-4: What is Truly at the Core of This Virtual God?
We worship ChatGPT like a virtual god, but what is truly at the core of this artificial intelligence technology?
442. Text Embedding Explained: How AI Understands Words
Large language models are a specific type of machine learning-based algorithm that understand and can generate language
443. AI and the Future of Space Exploration
As AI continues to evolve, we can expect to see even more innovative and groundbreaking applications of this technology in the years to come.
444. Top 10 Machine Learning Optimized Graphics Cards
How to choose the right graphics card and maximize the efficiency of processing large amounts of data and performing parallel computing.
445. TextStyleBrush Translates Text in Images While Emulating the Font
This new Facebook AI model can translate or edit the text in an image, while maintaining the same font and design as the original.
446. Generative AI, Fintech and Future of Financial Services
You know the hype is real when even the World Economic Forum writes that ChatGPT is just the start of the generative AI boom.
447. 27 Highest-Paying Cities in United States for Machine Learning Engineers
Machine learning engineers might be some of the most highly skilled software engineers. We take a look at how cities in the US compensate their ML engineers.
448. Is the Programming Market Oversaturated?
Every so often I hear that the programming market will be saturated eventually and we are all going to end up on the streets. Is this really true?
449. Why Robotic Process Automation Is Not Artificial Intelligence
Artificial intelligence has become a buzzword and is increasingly overused, designating even low-level automation. This leads to misinterpretation of its capabilities. It is worth making a distinction between real AI and robotic process automation (RPA).
450. The Implications of Open-Source AI: Should You Release Your AI Source Code Publicly?
In this article, I will share my thoughts on why it's better and safer to bring the new AI tech into the hands of business rather than release it into the wild.
451. Retraining Machine Learning Model Approaches
Retraining Machine Learning Model, Model Drift, Different ways to identify model drift, Performance Degradation
452. PyTorch vs TensorFlow: Who has More Pre-trained Deep Learning Models?
Given the importance of pre-trained Deep Learning models, which Deep Learning framework - PyTorch or TensorFlow - has more of these models available to users is
453. The Startup World in Generative AI
Or Gorodissky is VP of R&D at D-ID, a company that uses generative AI to create videos of photorealistic avatars.
454. Tired of Broken Chatbots? This AI Upgrade Fixes Everything
Discover how function calling transforms AI. Learn implementation differences between OpenAI and Azure OpenAI, with practical code examples.
455. The Weird and Wonderful World of AI Art
While the vast majority of developments in AI technology have centered around practical solutions such as self-driving cars and facial recognition, there's a growing number of artists using AI systems to develop new ideas for artistic projects and generate entirely unique pieces of work.
456. How to Turn Your Business into a Cognitive Enterprise with AI Technologies?
457. How to Build a Question and Answer Chatbot with Amazon Kendra and AWS Fargate
Amazon announced the general availability of Amazon Kendra a few weeks ago, Kendra is a highly accurate and easy to use enterprise search service powered by machine learning.
458. 5 Best Machine Learning Books for ML Beginners
Here is a list of the best books to learn machine learning for beginners to help build their careers in the ML Industry.
459. 11 Awesome (and Worrisome) Applications of AI
For years AI was touted to be the next big technology. Expected to revolutionize the job industry and effectively kill millions of human jobs, it became the poster child for job cuts. Despite this, its adoption has been increasingly well-received. To the tech experts, this wasn’t really surprising given its vast range of use cases.
460. AI-Driven Autonomous Agents - The Future of AI
Autonomous Agents get stronger every minute you read this article.
461. Say Goodbye to SEO - ChatGPT Steals the Show With Smarter Search
Search Engine Optimization (SEO) has been the backbone of an online search for over two decades now. But as Artificial Intelligence (AI) technology moves quickl
462. Quantum Machine Learning Using TensorFlow Quantum
INTRODUCTION
463. Why ML in Production is (still) Broken and Ways we Can Fix it
Machine Learning, Deep Learning development in production was still broken. ZenML, an extensible, open-source MLOps framework for production-ready ML pipelines.
464. AI Is Inherently Neutral - It Is Human Beings Who Are Biased, and the Machines Merely Replicate Them
since it is the developers that provide the data and train the model, it is them that can cause the model to become biased (intentionally or unintentionally)
465. Transformers: Age of Attention
Simple explanation of the Transformer model from the revolutionary paper "Attention is All You Need" which is the basis of many advanced AI systems.
466. Living in the world of AI - The Human Transformation
Today, if you stop and ask anyone working in a technology company, “What is the one thing that would help them change the world or make them grow faster than anyone else in their field?” The answer would be Data. Yes, data is everything. Because data can essentially change, cure, fix, and support just about any problem. Data is the truth behind everything from finding a cure for cancer to studying the shifting weather patterns.
467. Positional Embedding: The Secret behind the Accuracy of Transformer Neural Networks
An article explaining the intuition behind the “positional embedding” in transformer models from the renowned research paper - “Attention Is All You Need”.
468. Is The Modern Data Warehouse Dead?
Do we need a radical new approach to data warehouse technology? An immutable data warehouse starts with the data consumer SLAs and pipes data in pre-modeled.
469. What is OpenAI's Whisper Model?
Have you ever dreamed of a good transcription tool that would accurately understand what you say and write it down? Not like the automatic YouTube translation tools… I mean, they are good but far from perfect. Just try it out and turn the feature on for the video, and you’ll see what I’m talking about.
470. Everything You Need to Know About Google BERT
Google BERT will help you to kickstart your NLP journey by showing you how the transformer’s encoder and decoder work.
471. 7 Sneaky Ways Hackers Are Using Machine Learning to Steal Your Data
Machine learning is famous for its ability to analyze large data sets and identify patterns. It is basically a subset of artificial intelligence. Machine learning uses algorithms that leverages previous data-sets and statistical analysis to make assumptions and pass on judgments about behavior.
The best part, software or computers powered by machine learning algorithms can perform functions that they have not been programmed to perform.
472. Dall-E May Be Awesome, but It Still Can't Count.
OpenAI's "Dall-E" artificial intelligence can be very frustrating for some professional uses. Here are a few things that Dall-E just can't seem to do.
473. Is The Third AI Winter Coming?
People have countless fantasies about Artificial Intelligence. It has become the most popular theme in novels and movies. When we dream about AI, we often fancy a world with Iron Man and his intelligent assistant J.A.R.V.I.S (or it’s replacement FRIDAY); Baymax from Big Hero 6; or the high-tech adult theme park from Westworld.
474. Is Quantum Cognition the Path to Strong AI (or Artificial General Intelligence)?
Quantum cognition as the path to explaining the mind
475. ChatGPT is Amazing. And It is FREE!
GPT, or Generative Pretrained Transformer, is a type of language model that uses deep learning to generate human-like text.
476. Using Machine Learning to Build a Ride Acceptance Model for Uber
Objective: Predict if a driver will accept a ride request or not and find the probability of acceptance.
477. How Can Machine Learning Predict the Stock Market?
Artificial intelligence is changing the world as we know it. Form self-driving cars to weather predictions. Now it's taking on the stock market. Here's how.
478. Why Use Kubernetes for Distributed Inferences on Large AI/ML Datasets
This blog provides you with some strong rationale to use Kubernetes on large AI/ML datasets on which distributed inferences are performed. Loop in for more.
479. How to Build a Customer Service Chatbot with Python, Flask, and Pinecone
What if a customer asks a question, you could easily find previously asked similar questions and answers that could help them?
480. 'El transformador ilustrado' una traducción al español
<meta name="monetization" content="$ilp.uphold.com/EXa8i9DQ32qy">
481. Top 9 Free Beginner Tutorials for Machine Learning (ML)
This post includes a round-up of some of the best free beginner tutorials for Machine Learning.
482. The Real World Potential and Limitations of Artificial Intelligence
No longer does artificial intelligence only exist in sci-fi movies and books about dystopian futures. It’s in the here and now, continuously transforming the way in which we live and work.
483. Everyone's Using the Wrong Algebra in AI
From Tesla phantom braking to LLM hallucinations, the root bug is first-order math. We explain how dual/jet numbers unlock scalable second-order AI.
484. How to Remove Gender Bias in Machine Learning Models: NLP and Word Embeddings
Most word embeddings used are glaringly sexist, let us look at some ways to de-bias such embeddings.
485. Use plaidML to do Machine Learning on macOS with an AMD GPU
Want to train machine learning models on your Mac’s integrated AMD GPU or an external graphics card? Look no further than PlaidML.
486. AI vs. Sarcasm: Will It Ever Understand?
Exploring AI's understanding of sarcasm and emotions, revealing the complexity of human interactions and challenges in sentiment analysis.
487. 4 Fashion Brands That Included Smart Vending Machines In Their Marketing Strategy
Have you ever wanted to buy a dress from a vending machine? See how brands are using smart vending to customize the shopping experience.
488. Beat The Heat with Machine Learning Cheat Sheet
If you are a beginner and just started machine learning or even an intermediate level programmer, you might have been stuck on how do you solve this problem. Where do you start? and where do you go from here?
489. AI vs ML: What's the Difference?
Learn the distinctions between AI and ML with vivid examples.
490. How To Retrieve Company Data With Python and yfinance
Use Python for Finance to do financial analysis, such as retrieve historical prices, calculate moving average and plot daily returns.
491. Small Object Detection in Computer Vision: The Patch-Based Approach
How to carry out small object detection with Computer Vision - An example of finding lost people in a forest.
492. Text-to-Image: How AI Illustrates the War in Ukraine and What You Need to Generate Your Own Art
Text-to-Image: how AI illustrates the war in Ukraine and what you need to know to generate your own
493. How to Get Started With Embeddings
Getting started with embeddings using open-source tools.
494. A Detailed Overview of How AI Detectors Work
Interested in finding out how AI detection works? Well, you are in for a treat. I'll keep it as simple as possible so that anyone can understand.
495. A Guide to Using Apache Cassandra as a Real-time Feature Store
This guide explores real-time AI and the unique performance and cost attributes of Cassandra that make it an excellent database for a feature store.
496. How to Build a Training Pipeline on Multiple GPUs
In the current big data regime, it is hard to fit all the data into a single CPU.
497. Credit Card Fraud Detection via Machine Learning: A Case Study
A machine learning guide on how to identify fraudulent credit card transactions by using the PyOD toolkit.
498. Adversarial Examples In Machine Learning Explained
There are easy ways to build adversarial examples that can fool any deep learning model and create security issues no matter how complex the model is.
499. Anscombe’s Quartet And Importance of Data Visualization
Anscombe’s quartet comprises four data sets that have nearly identical simple descriptive statistics, yet have very different distributions and appear very different when graphed.
— Wikipedia
500. Darwin's Hybrid Intelligence to Align AI & Human Goals for Startups & VCs
This post is part of the Hacker Noon Shareholder Series, where we interview some of the super-investors who made the site you're on right now possible.
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