Let's learn about Ml via these 320 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.
Known for its polymorphic Hindler-Milner type system, Ml is a functional general purpose programming language.
1. Understanding the Two-Tower Model in Personalized Recommendation Systems
Understanding how the two-tower model is used for the retrieval stage of recommendation systems.
2. Understanding Stochastic Average Gradient
Techniques like Stochastic Gradient Descent (SGD) are designed to improve the calculation performance but at the cost of convergence accuracy.
3. 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.
4. 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.
5. 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.
6. 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.
7. 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!
8. 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.
9. 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.
10. 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.
11. 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.
12. 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.
13. 30 App Development Ideas for Startups (AI/ML, Blockchain, AR/VR)
As an IT sourcing analyst with ValueCoders a leading offshore IT outsourcing firm, I have helped several startups, SMEs, and enterprises build their mobile apps.
14. 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.
15. The Promise and Potential of LLM in Crypto
We cover 8 main potential applications of LLM in the crypto space. LLM can benefit all members in the crypto space; LLM can be integrated into existing projects
16. 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.
17. 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.
18. 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.
19. Alan Turing Was Right—a Machine Could Think
The idea that machines could think occurred to the very first computer builders and programmers.
20. 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.
21. 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.
22. 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.
23. AI vs. Machine Learning: Key Differences Explained
Eliminate your confusion between AI and ML, two different topics that are often confused for one another.
24. 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.
25. AI-Powered Healthcare: Bridging the Personnel Gap
The healthcare industry has been grappling with a severe shortage of personnel...
26. Manipulate Images Using Text Commands via this AI
Manipulate Real Images With Text - An AI For Creative Artists! StyleCLIP Explained
27. 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.
28. How AI Has Impacted Product Management
In this article, I want to talk more about the broader influence of AI in product management tools, more than I’d like to go into chatbot-specific applications.
29. How Machine Learning is Used in Astronomy
Is Astronomy data science?
30. 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.
31. Advanced Linux Shell with AI-powered Features
An AI-powered Linux shell that can do what you say was made possible with OpenAI GPT-2 language model.
32. 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.
33. The Best Programming Languages for Working with AI
You will require coding skills if you want to work in the field of artificial intelligence (AI). How do you begin? and Which programming language to use?
34. 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.
35. 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)
36. 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.
37. How to Build Your Own Automated Self Checkout Service
Brick-n-mortar retailers, learn how to implement an AI-powered autonomous checkout from smart vending machines and kiosks to full store automation.
38. 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.
39. How Do Banks Predict Expected Credit Losses?
This article explains bank reserves, their importance, how banks assess them, and the role of machine learning in improving reserve evaluation.
40. 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.
41. 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.
42. 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.
43. Will AI Put Product Managers Out of Work?
Many people believe that AI might eventually take over our jobs. But is this really true? Can AI do everything as well as humans can?
44. 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.
45. 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.
46. 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.
47. How to Detect Language and Translate text in Android with Firebase ML Kit
Detect Language and Translate text in Android with Firebase ML Kit
[48. Differential Privacy with Tensorflow 2.0 : Multi class Text Classification
Privacy](https://hackernoon.com/differential-privacy-with-tensorflow-20-multi-class-text-classification-privacy-yk7a37uh)
Introduction
49. How to Perform Sentiment Analysis with Amazon Comprehend
How to analyze the sentiments from a text using AWS services like Amazon Comprehend, AWS IAM, AWS Lambda, and Amazon S3.
50. 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.
51. The Engineer's Handbook for Effective ML Product Roadmapping
The story describes how to write a product roadmap if you are a machine learning engineer: difficulties and best practices
52. From Satellite Signals to Neural Networks
See how Andrei Shcherbinin built production-ready ML systems with 12x faster attribution, 95% chatbot automation, and stronger monitoring.
53. 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.
54. 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.
55. GPT-4, Llama-2, Claude: How Different Language Models React to Prompts
Exploring the unique behaviors of different Large Language Models (LLMs) and mastering advanced prompting techniques!
56. Playing God in the Fucking Metaverse
Web 3.0 for dummies, by dummies.
57. 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
58. 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
59. Building Handwritten Digits Recognizer using Support Vector Machine
Handwriting Recognition:
60. A Practical Guide to Measuring Business Impact in AI/ML Projects
Measuring AI impact made clear: experiments, causal methods, and sanity checks to separate real improvement from coincidence.
61. 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.
62. The Rise of MLOps: What We Can All Learn from DevOps
The MLOps Conference took place earlier this week at Hudson Mercantile in New York City. Experts from the New York Times, Twitter, Netflix and Iguazio, the host company, spoke about best practices and machine learning implementation throughout a variety of different organizations.
63. 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).
64. 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
65. AI Facts Every Dev Should Know: Artificial intelligence is older than you, probably
The hype around AI is growing rapidly, as most research companies predict AI will take on an increasingly important role in the future.
66. The Essential Architectures For Every Data Scientist and Big Data Engineer
Comprehensive List of Feature Store Architectures for Data Scientists and Big Data Professionals
67. 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
68. 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.
69. 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.
70. 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.
71. The Evolution of Decision Trees: From Shannon Entropy to Modern Applications and Specialties
Discover the evolution and importance of decision trees in machine learning, from their early beginnings in the 1960s to their widespread use in modern ensemble
72. 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.
73. 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?
74. 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.
75. 4 Ways Startups Can Overcome Implementation Challenges of Machine Learning
Machine learning is the best method of data analysis. It also automates the creation of analytical business models. This is the reason why machine learning plays an important role in the growth of a business. Hence, your business will probably need new and highly inspired ideas to deploy machine learning solutions into your business. However, the implementation of machine learning can bring several challenges.
76. Machine Learning Magic: How to Speed Up Offline Inference for Large Datasets
Running inference at scale is challenging. See how we speed up the I/O performance for large-scale ML/DL offline inference jobs.
77. Game Theory Meets AI and NLP
With game theory, players come to a point where optimal decision-making is reached. This is very important n the field of ML, AI, or NLP. Find out how!
78. Black Mirror's 'Be Right Back' in Real Life: Clone Yourself as a Chatbot
Replika AI has created a platform where anyone, including people with zero knowledge of machine learning, can create and train a chatbot of their own.
79. Machine Learning 101: How And Where To Start For Absolute Beginners
This post covers all you will need for your Journey as a Beginner. All the Resources are provided with links. You just need Time and Your dedication.
80. Why LLM Chatbots Won’t Be Replacing Language Teachers Any Time Soon
LLM chatbots are powerful tools for language learning, offering interactive and affordable experiences. However, human teachers are still essential.
81. Semi-Supervised Machine Learning Algorithms
82. Galactica is an AI Model Trained on 120 Billion Parameters
On November 15th, MetaAI and Papers with Code announced the release of Galactica, a game-changer, open-source large language model trained on scientific knowledge with 120 billion parameters.
83. Extract Prominent Colors from an Image Using Machine Learning
This article explains how I found a nice and simple algorithm to extract prominent colors out of an image.
84. Quantum Computing Will Transform the Logistics Industry Within the Next Decade
Quantum computing is going revolutionizes logistics by addressing complex challenges in the supply chain and transportation sectors.
85. Image Style Transfer And Video Transformation In EbSynth
86. Why Tesla’s Optimus Is a Big Step on the Way to AGI
LaMDA-like large language models with Tesla Optimus-like robots will be the next big step on the way to Artificial General Intelligence.
87. A New Programming Language For AI: Linear Regression, But With Mojo Language
Mojo is a new programming language for AI development. The language is being developed by the Modular company.
88. The State of AI in 2022: An End-of-Year Recap of the Machine Learning Industry
An 8-minute AI rewind with results and limitations of all the hottest AI models shared in 2022!
89. Trade Crypto with Machine Learning Based On Google Trends
90. Top AI and ML YouTube Channels for Data Scientists to Subscribe to
Subscribe to these Machine Learning YouTube channels today for AI, ML, and computer science tutorial videos.
91. Applying Machine Learning to Crypto-Sphere: The Good and the Bad Aspects
Anyone who has traded cryptocurrencies or invested in Bitcoin stocks before has been frustrated by the difficulty involved with trying to predict market trends.
92. PULSE: Photo Upsampling Makes Blurry Faces 60 Times Sharper
The new PULSE: Photo Upsampling algorithm transforms a blurry image into a high-resolution image.
93. Integrate AI into Data Mapping to Drive Business Decision Making
Prior to analyzing large chunks of data, enterprises must homogenize them in a way that makes them available and accessible to decision-makers. Presently, data comes from many sources, and every particular source can define similar data points in different ways. Say for example, the state field in a source system may exhibit “Illinois” but the destination keeps it is as “IL”.
94. Here’s How OpenAI is Perpetuating Unhealthy Stereotypes
There has been a lot of buzz about OpenA GPT-3, now having the largest neural network. Does it mean the AI problem has been solved?
95. Machine Learning Meets HR: Predicting Employee Attrition with PyCaret
It is time to start talking how machine learning can be leverage in AR. Today I'm walking you through how PyCaret can be used to predict employee attrition.
96. How to Create an End-to-end Machine Learning Workflow
How to develop ML models efficiently? Stick to a well-structured workflow and your project will breeze through the stages.
97. The Notions behind “Model-Based” and “Instance-Based” Learning in AI & ML
A prelude article elucidating the fundamental principles and differences between “Model-based” & “Instance-based” learning in the branches of Artificial Intelligence & Machine learning.
98. Here’s Machine Learning for NFTs: DeepNFTValue
DeepNFTValue applies AI/ML to NFT valuation. The team uses Ensemble and DNN to predict NFT future price. Similar ideas are widely used in the stock market.
99. Engineering a Trillion-Parameter Architecture on Consumer Hardware
A deep dive into how one researcher trained a Trillion-Parameter-Scale AI model on an RTX 4080 laptop, proving the democratization of of LLMs is possible.
100. 8 Companies Using Machine Learning in Cool Ways
When asked what advice he'd give to world leaders, Elon Musk replied, "Implement a protocol to control the development of Artificial Intelligence."
101. Introduction 5 Different Types of Text Annotation in NLP
Natural language processing (NLP) is one of the biggest fields of AI development. Numerous NLP solutions like chatbots, automatic speech recognition, and sentiment analysis programs can improve efficiency and productivity in various businesses around the world.
102. Debunking 4 Common Myths About Machine Learning
Machine learning is a subset of artificial intelligence that involves the use of algorithms and statistical models to enable computers to improve them.
103. How to Implement Digital Twin Architecture
What technologies are behind the digital twin and how to reasonably approach its creation? Discover a detailed explanation in this article.
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104. The One Data Science Project Idea That’ll Impress Interviewers
Let’s talk about the one and only project you need to build, that’ll help you gain fullstack data science experience, and impress interviewers on your interviews if your goal is to jumpstart your career in data science.
105. Getting Started with Micrograd TS
A TypeScript version of karpathy/micrograd. A tiny scalar-valued autograd engine and a neural net on top of it.
106. Meet The Entrepreneur: Alon Lev, CEO, Qwak
Meet The Entrepreneur: Alon Lev, CEO, Qwak
107. Machine Learning Algorithms Explained
Can you remember five examples of machine learning in real life? We share impressive examples of ML that we use every day that may not be obvious to you.
108. Data Preparation for Machine Learning: A Step-by-Step Guide
Many businesses assume that feeding large volumes of data into an ML engine is enough to generate accurate predictions.
109. A Summary and Review of The Ethical Algorithm
A summary and review of: The Ethical Algorithm: The Science of Socially Aware Algorithm Design by Aaron Roth and Michael Kearns.
110. How to Build the Perfect CV to Land a Data Science Role
Looking to make your data scientist resume more attractive to employers?
111. Difference Between Boosting Trees: Updates to Classics With CatBoost, XGBoost and LightGBM
Explore boosting trees' evolution: from AdaBoost to XGBoost, LightGBM, and CatBoost. Learn key updates & how to choose the right library for your needs.
112. Top Five Benefits of Using Machine Learning For Demand Forecasting
Machine learning has become a vital component to get solutions in everyday life. It is adding intelligence in every product we are using today. Marketing software and demand forecasting are using ML to a great extent.
113. A Product Manager's 600-Word Guide to Machine Learning
Machine learning (ML) is a technology or field of computer science that learns from historical data to make accurate predictions or decisions.
114. What Do Sex and AI Have in Common?
Reproduction is the purpose of a species, first appearing 1.2 billion years ago in the evolution of animals. But it's not our only purpose—or we'd be no different from other mammals. No, we are creative, romantic, and most of all, curious beings reaching for the stars.
115. Using the LDA Algorithm for Websites
Have you ever had to find unique topics in a set of documents? If you have, then you’ve probably worked with Latent Dirichlet Allocation (or LDA).
116. Most Useful ML Ops Applications
Many ML Ops tools allow overseeing the entire machine learning model life cycle. Here are some of the most worthwhile ones to consider.
117. 5 Papers on Face Recognition Every Data Scientist Should Read
Facial recognition, is one of the largest areas of research within computer vision. This article will introduce 5 face recognition papers for data scientists.
118. Top 8 Machine Learning Content Creators on YouTube
Here are the top Machine Learning content creators on YouTube to follow for tutorials, deep learning, and more.
119. How Big Data and AI Help People Make Smarter Investments
Big data, artificial intelligence, and machine learning are some of the hottest technologies out there. Well, machine learning has existed since the late 1950s, and big data got first coined in 2005. However, it is only in the last decade, or so that computer engineers, scientists, and corporations have tried widespread implementations of these technologies.
120. 7 AI-powered Chatbots
121. Top 15 Datasets for Autonomous Driving
A2D2, ApolloScape, and Berkeley DeepDrive are among the best autonomous driving datasets available today.
122. Build an Article Recommendation Engine Using Machine Learning
In this article, we’ll build a Python Flask app that uses Pinecone — a similarity search service — to create our very own article recommendation engine.
123. 6 Incredible Photo Editing AI Tools You Need to Know
The intelligence exhibited by machines or software is known as artificial intelligence. In recent years, it has seen extensive use in the field of images.
124. A Quick Guide to Image Processing in Computer Vision Using OpenCV
The image processing library which stands for Open-Source Computer Vision Library was invented by intel in 1999 and written in C/C++
125. My Experiments And How To Start with Machine Learning
[https://hackernoon.com/images/dJ7MzRYbq8et9JjFyKAEWhkCfPO2-2023yst.jpg]
First of all, let me be clear, what this blog-post is and what it isn’t. This
126. AI and Cryptocurrency Interactions You May Not Know About
You often hear AI thrown into a sentence with Bitcoin or blockchain technology. Often this generates more interest in cryptocurrencies as AI has been the “next big thing” for quite some time now.
127. Easy Data Visualization with AutoViz [Maybe Just a Quick One]
Easy data visualization with AutoViz.
128. Why and How to Build a Custom Recommendation Engine
129. MLOps and ML Infrastructure on AWS
AI companies have been struggling with Big Data environments and analytical and machine learning pipelines for years. Organizations expect to start driving value from AI and machine learning within a few months, but, on average, it takes from four months to a year to even launch an AI MVP.
130. What is Data-Centric AI?
What makes GPT-3 and Dalle powerful is exactly the same thing: Data.
131. How to Get Better Datasets for Your Computer Vision Task
Here are some tips to improve your dataset collection
132. A Gentle Introduction to Data Augmentation
Data augmentation is a set of techniques used to increase the amount of data in a machine learning model by adding slightly modified copies of existing data.
133. Analyzing Customer Reviews with Natural Language Processing
In this article, we build a machine-learning model to guess the tone of customer reviews based on historical data.
134. Your First Quantum Machine Learning Course
Excited about quantum computing? Then have a look at quantum machine learning, you'll be ecstatic. It is a glorious time to be alive.
135. 7 Challenges in Marketing AI & Machine Learning Solutions
This article will help our readers to identify and understand the challenges faced by the AI development companies to market the AI & ML products.
136. Use Up-Sampling and Weights to Address Imbalance Data Problem
Have you worked on machine learning classification problem in the real world? If so, you probably have some experience with imbalance data problem. Imbalance data means the classes we want to predict are disproportional. Classes that make up a large proportion of the data are called majority classes. Those that make up a smaller portion are minority classes. For example, we want to use machine learning models to capture credit card fraud, and fraudulent activities happens approximately 0.1% out of millions of transactions. The majority of regular transactions will impede the machine learning algorithm to identify patterns for the fraudulent activities.
137. Top 12 Javascript Libraries for Machine Learning
Rapidly evolving technologies like Machine Learning, Artificial Intelligence, and Data Science were undoubtedly among the most booming technologies of this decade. The s specifically focusses on Machine Learning which, in general, helped improve productivity across several sectors of the industry by more than 40%. It is a no-brainer that Machine Learning jobs are among the most sought-after jobs in the industry.
138. How to Add Training Data to Build a More Generic ML Model
You can easily make changes to your dataset using DVC to handle data versioning. This will let you extend your models to handle more generic data.
139. Challenges in successful implementation of Machine Learning AI in SMEs
There is a general debate going on how ethical or unethical the use of AI is, however not many people are talking about the challenges in adoption of AI by Small and Medium-sized enterprises. So, before we go onepondering about how people will lose their jobs due to AI , or before weactually start looking for new careers without actually knowing what AI is about, let me take you through a few challenges we are facing in the implementation of Machine learning and Deep learning programs and apps developed on AI platforms, in the real world especially by the majority of businesses around the globe.
140. From Data to Dollars: Exploring the Role of AI and ML in Sales
This blog will discuss what role artificial intelligence and machine learning play in sales.
141. Using AI for Fraud Detection
Just as your average cyberattack has grown more sophisticated, so have the avenues for fraud. To keep up with these threats, we can use AI for better detection.
142. Powerful Data Analytics Software: SAS Three-Tier Architecture Install on AWS Environment
SAS is an s a powerful analytics platform that allows users to perform complex data analysis and modeling tasks.
143. Infinite Nature: Fly Into a 2D Image and Explore it as a Drone
The next step for view synthesis: Perpetual View Generation, where the goal is to take an image to fly into it and explore the landscape!
144. Maggie: The Saga of a Baby Translator AI Startup
Follow the journey of two classmates as they build a baby translator app, facing technical challenges and the harsh realities of startup life.
145. ⛓ Check the first ML Value Chain Landscape shaped by ML practitioners!
The first ML Value Chain Landscape shaped by ML practitioners
146. How Machine Learning Generates Income for Businesses
Machine learning in business: applying ML to solve business problems. How can machine learning optimize operational procedures and general income?
147. Data Science with Substance: Best Massive Open Online Courses
Benjamin Obi Tayo, in his recent post "Data Science MOOCs are too Superficial," wrote the following:
148. Encoding Categorical Data for ML Algorithms
Encoding is a technique used to convert categorical data to numerical representations to be able to use the data in machine learning algorithms.
149. Predicting Links in Graphs with Graph Neural Networks and DGL.ai
Learn to build a model for predicting new links in a social graph, demonstrating the use of Graph Neural Networks and DGL for link prediction.
150. ChatGPT Is Making the Internet More Fun and Less Confusing
This is a short story about the rise of ChatGPT :)
I hope you like it.
151. From TF to TFLite: Deploying ML Models on Mobile [Part 2]
This is part 2 of the two-part article on deploying ML models on mobile. We saw how to convert our ML models to TfLite format here. For those of you who came here first, I recommend you click on the above link to get the whole picture. If you just want the android part ,the demo app we are building has a GAN model generating handwritten digits and a classifier model predicting the generated digit.
152. 3 Key Tools for Deploying AI/ML Workloads on Kubernetes
Organizations know the importance of getting the full application stack on Kubernetes. Artificial intelligence is next.
153. Mastering AI Operations
The data whisperer is the function sitting between the business and the technologists.
154. The Impact of Artificial Intelligence on Copywriting
Learn how artificial intelligence is impacting copywriting and helping businesses grow.
155. Grow Your AI While Cutting Machine Learning Costs: SageMaker Will Now Manage Spot Instances For You.
In 2018, OpenAI released a study that found the compute power used by the largest AI training runs has doubled every 3.5 months since 2012. From autonomous vehicles to DNA analysis, there's little doubt the demand for machine learning and AI is driving the supply of increased computing power today.
156. Start With Machine Learning
We all have to deal with data, and we try to learn about and implement machine learning into our projects. But everyone seems to forget one thing... it's far from perfect, and there is so much to go through! Don't worry, we'll discuss every little step, from start to finish 👀.
157. 5 Problems That Artificial Intelligence Cannot Yet Solve
Humanity has recently begun to rely more and more on the help of AI. But can we really rely on such technology today?
158. How Document Classification Can Improve Business Processes
The process of labeling documents into categories based on the type of the content is known as document classification. It can also be defined as the process of assigning one or more classes or categories to a document (depending on the type of content) to make it easy to sort and manage images, texts, and videos. Document classification can be done using artificial intelligence, machine learning, and python.
159. How to Colorize a Black and White Photo
Colorizing black and white photos using DeOldify and Python
160. The Dawn of the Transformer Neural Networks
Why are GPT-3 and all the other transformer models so exciting? Let's find out!
161. Emotion AI, ML, and Deep Learning: A Brief Introduction
162. How to Win a Kaggle Competition: Box Office Prediction Competition
Introduction
163. An Intro to AI Startups for Complete Beginners
AI Startups have set the ball rolling for a revolution in multiple industries: Find out how to get billions of dollars from investors for your AI startup.
164. Using Human-in-the-Loop Approach in Machine Learning
Artificial intelligence makes mistakes. Significant, even life-altering ones. So, how can we still get benefits of AI while eliminating these types of errors.
165. Automation Isn’t Really a Bad Thing for Employees
Learn more about automation, its role in today’s workforce and whether it will have positive or negative implications for American workers.
166. CVPR 2021 Best Paper Award: GIRAFFE Controllable Image Generation
Using a modified GAN architecture, they can move objects in the image without affecting the background or the other objects!
167. Introduction to Human In The Loop Machine Learning
What is Human In The Loop?
168. Choosing Python for Web Development: Top 16 Pros and Cons
169. Key Aspects of Machine Learning Operations, Explained
If you have ever worked or currently working in the IT field, then you definitely faced the common term «machine learning.
170. Reducing Docker Container Start-up Latency: Practical Strategies for Faster AI/ML Workflows
Container start-up latency can significantly slow down AI/ML workflows and degrade user experience in interactive environments.
171. How Technology and Finance are Changing in Today’s World?
Technology and finance provide news, analysis, and insights on various business-related topics, including finance, upcoming global situations, and scopes.
172. Language Modeling - A Look at the Most Common Pre-Training Tasks
This article is about putting all the popular pre-training tasks used in various language modelling tasks at a glance.
173. Accelerating Excavation and Refinement of Data Gold Mines
Unlock the potential of data-driven decision-making with generative AI and NLP.
174. Partial Dependence Plots: How to Discover Variables Influencing a Model
Quick techniques on how to find which variables are influencing the model results and by how much and how to visualize using Partial dependence plots.
175. Why "Big Data" is No Longer Relevant in the Age of Machine Learning and Deep Learning
Discover why "Big Data" is no longer relevant with the rise of Machine Learning and Deep Learning. Learn how these technologies transform data analytics.
176. AI Makes Tech Less Terrifying For “Word People”
Discover how AI is reshaping the world for writers and creatives. Dive into how Natural Language Interfaces are making tech more accessible.
177. How AI and Machine Learning are Reshaping SaaS FinTech
AI and Machine learning are awesome pieces of technology. With applications in many fields, find out how AI and ML are reshaping the Saas fintech landscape.
178. Introducing ML News
I know.
179. How Fraud Will Be Fought in the Metaverse
Everybody is talking about the metaverse. But how exactly will companies protect themselves against fraud in this new virtual world? AI is the answer.
180. The Most Detailed Guide On MLOps: Part 2
Not all market players consciously approach the choice of hardware for the ML-system. This is especially noticeable in terms of GPU selection.
181. What Are Generative Adversarial Networks and What Can They Achieve? [ELI5]
Not long ago, the wider sentiment in the AI industry was that "AI can't be creative." Even today, some people hold to that view, though AI is being used to compose music, poems, sculptures, and draw paintings, like the one below:
182. Expanding Design Exploration: Exploring Feature Spaces Beyond Parametric Boundaries
The project presented here marks a significant step forward in how designers can interact with complex datasets and uncover new possibilities in their work.
183. Dr. Alexa Is Ready to See You: Are You Ready for AI Doctors?
Artificial Intelligence (AI) has rapidly gone from something we only see in sci-fi movies to a technology we interact with every day. From making product recommendations to finishing your sentences, AI is everywhere.
184. Building AI Products with Big Data
Credits: Thanks to our sponsor Amazon, the Advancing Women in Product Team: Keshav Attrey, Reeba Monachan Attrey, Kanika Kapoor, Alok Gupta, Jackie Yen, our AWIP volunteers and our panelists.
185. CLIP: An Innovative Aqueduct Between Computer Vision and NLP
A rudimentary article describing the concept behind the "CLIP" algorithm in deep learning, its approach, implementation, scope & limitations.
186. When Did Beyoncé Start Becoming Popular? - Tackling One of the Most Common Problems in NLP: Q/A
Hello! Today I’d like to explain how to solve one of the most troublesome tasks in NLP — question answering.
187. The Basics Of Natural Language Processing in 10 Minutes
Do you also want to learn NLP as Quick as Possible ? Perhaps you are here because you also want to learn natural language processing as quickly as possible, like me.
188. 7 Vital Steps in the Machine Learning Life Cycle
This is a framework for using machine learning in your business.
189. 5 Skills Every Successful ML Engineer Should Have
Uncover the five essential skills every successful machine learning engineer should have. Boost your ML engineering career with these invaluable insights.
190. Setting Up Prometheus Alertmanager on GPUs for Improved ML Lifecycle
Quantity and variety of data fuel the rise of complex and sophisticated ML algorithms to handle AI workloads.
191. Data Pipelines and Expiring Dictionaries
Designing a data pipeline comes with its own set of problems. Take lambda architecture for example. In the batch layer, if data somewhere in the past is incorrect, you’d have to run the computation function on the whole (possibly terabytes large) dataset, the result of which would be absorbed in serving layer and are reflected.
192. "We Know About AI's Ability To Remember, But Forget About Its Ability To Forget." - Valeria Sadovykh
As our world approaches the time where artificial intelligence becomes as widespread as electricity, we sat down with Valeria Sadovykh, a leading expert in the decision making and decision intelligence aspects of AI. Valeria holds a Ph.D. from the University of Auckland Business School and has over 10 years of experience focusing her efforts on emerging technologies with PwC in New Zealand, Singapore, and the US.
193. 242 Stories To Learn About Ml
Learn everything you need to know about Ml via these 242 free HackerNoon stories.
194. How To Use Firebase Machine Learning Kit
There changed into a time when gaining knowledge of and enforcing device learning changed into no longer an smooth task. And if we talk about implementing the device getting to know inside the cellular devices then it turned into now not possible most effective due to the fact the execution of the heavy algorithm desires heigh computing power. But as we know, mobile generation has grown exponentially in the past few years.
Firebase is one of them. It has recently announced a new characteristic that's Firebase Machine Learning package. In this tutorial, I will explain everything approximately it in detail. I will also show you a way to Integrate the Firebase system getting to know package to your android app.
195. How No Code ML Can Create an Impact on Businesses
There’s a process for solving business problems via machine learning. If you Google “learn machine learning,” you’ll find a bunch of guides, online courses, and such that walk you through the coding languages of ML and the processes it takes to solve data predictions. You conclude it takes a lot of time to learn technical machine learning.
196. Announcing ModelDB 2.0 release
Since we wrote ModelDB 1.0, a pioneering model versioning system, we have learned a lot and adapting it to the evolving ecosystem became a challenge. Hence we decided to rebuild from the ground up to support a model versioning system tailored to make ML development and deployment reliable, safe, and reproducible.
197. Unleashing the Power of Julia: Deep Learning Capabilities Explored Through 5 Case Studies
If you really want to discover the power of Julia, check out this article. Code simplicity and readability are off the charts!
198. Universal Data Tool: New Skeletal/Pose/Landmark Annotation, Dutch, and Convert Options
For those who haven’t heard of the Universal Data Tool, it is an open-source web or desktop program to collaborate, build and edit text, image, video, and audio datasets with labels and annotations.
199. Machine Learning Trends Businesses Should Know In 2020
Have you ever considered how much data exists in our world? Data growth has been immense since the creation of the Internet and has only accelerated in the last two decades. Today the Internet hosts an estimated 2 billion websites for 4.2 billion active users.
200. Learning From Machine Learning: Why Green Tests Are Not Good News
Making all your tests green is essentially overfitting. Instead of patching your code to make it look like it works, you should measure how often it fails.
201. Using Image Classification for Fitness and Dieting Apps
Body management is desired yet hard. Knowing how much the daily take-in is can also be a challenge. Read on to use ML tech to overcome these challenges.
202. Building an Efficient AI Platform for Data Preprocessing and Model Training
Lei Li, AI Platform Lead, and Zifan Ni, Senior Software Engineer from Bilibili, share how they increased the training efficiency on their AI platform.
203. How to Deploy ML Workflows on LKE with Kubeflow
Introduction
204. MODEL-CENTRIC vs DATA-CENTRIC Approaches in Machine Learning
Machine learning is an area of artificial intelligence (AI) and computer science that focuses on using data and algorithms to mimic the way humans learn
205. Extraction of Relevant Text From Scientific Papers Using Machine Learning
There’s a huge potential in the domain of vital information extraction and summarization of scientific papers that I believe is under-researched.
206. DALL·E 2 Pre-Training Mitigations
You’ve all seen amazing-looking images like these, entirely generated by an artificial intelligence model. I covered multiple approaches on my channel, like Craiyon, Imagen, and the most well-known, Dall-e 2.Most people want to try them and generate images from random prompts, but the majority of these models aren’t open-source, which means we, regular people like us, cannot use them freely. Why? This is what we will dive into in this video...
207. The ML Product Manager: Building AI-powered Solution
I spoke to Gleb Sinev, an ML product manager with experience at companies like Surfingbird, Handl, and Mantika.ai who offered valuable insights.
208. 10 Computer Vision Startups on Product Hunt with the Most Upvotes
From self-driving cars and facial recognition to AI surveillance and GANs, computer vision tech has been the poster child of the AI industry in recent years. With such a collaborative global data science community, the advancements have come both from research teams, big tech, and computer vision startups alike.
209. How to Set Up an iPad for Machine Learning Development
If you have an iPad and want to use it as a development tool, you only need to complete 5 steps before using it. In this guide, you'll learn how to:
210. Poor Data Quality is the Bane of Machine Learning Models
An examination of the importance of data quality, how it can present itself in a dataset, and how it can impact machine learning models.
211. 5 Essential Product Classification Papers for Data Scientists
Product categorization/product classification is the organization of products into their respective departments or categories. As well, a large part of the process is the design of the product taxonomy as a whole.
212. An Introduction to Adversarial Attacks and Defense Strategies
213. Understanding Conversational AI: As Chat Enabled Customer Service
Technological innovations are necessary to cope up with the customer demands. Customers nowadays use multiple channels to access the services from a business. Thus, they expect multiple channel customer service from companies.
214. The Problem with Data Science Interviews
215. Data Reduction in Preparation for Lightweight Machine Learning: Applied in Foreign Exchange Trading
- Introduction
216. 15 Must-read Machine Learning Articles for Data Scientists
As always, the fields of deep learning and natural language processing are as busy as ever. Despite many industries being hindered by the quarantine restrictions in many countries, the machine learning industry continues to move forward.
217. 8 Open-source NLP Tools You Should Try
The write-up is about various free open-source NLP tools available in the market which any developer can use as per the requirement.
218. 7 Strategies to Reduce Training Data Acquisition Cost
Optimize your machine learning models without breaking the bank. These 7 effective strategies will help you acquire training data at a lower cost
219. A Brief Introduction to 5 Predictive Models in Data Science
Predictive Modeling in Data Science is more like the answer to the question “What is going to happen in the future, based on known past behaviors?”
220. Machine Learning For Fraud Prevention - Why It's The Best Tool Yet
With the development and sophistication of modern technologies, life has become much more comfortable. While it was considered impossible in the past to conduct complicated operations simultaneously, a computer made this task way easier.
221. The Role of Supervised Fine-Tuning in AI
Supervised fine-tuning explained: how it aligns pretrained AI models for task-specific behavior, production reliability, and structured outputs.
222. Understanding Graph Neural Networks (GNNs): Intro for Beginners
Beginner intro to graph neural networks!
223. Enterprise AI Has Been Failing, Here’s How It Can Recover
Over the last decade, AI has evolved into an all-purpose term for any accomplishments of computer algorithms that formerly required human reasoning and thought
224. What is Natural Language Processing? A Brief Overview
Natural language processing (NLP) is a subfield of artificial intelligence. It is the ability to analyze and process a natural language.
225. 11 Real-World Applications of Machine Learning in Ecommerce
In this blog, we look at 11 key use cases of machine learning in ecommerce that are currently taking the cake.
226. A Pill a Day: What AI Can REALLY Do in the Pharma Sector
It’s not surprising that pharmaceutical companies turn to medical AI services to cut the costs and time required for drug development.
227. An Intro to Edge Computer Vision: Technologies, Applications, Use Cases and Key Models
introduction to computer vision technologies, applications, use cases and key models.
228. How To Build Links Detector That Making Links in Your Book Clickable
How I built a link detector for your smart phone to browse links printed in books.
229. CVPR 2022 Best Paper Honorable Mention: Dual-Shutter Optical Vibration Sensing
TLDR: They reconstruct sound using cameras and a laser beam on any vibrating surface, allowing them to isolate music instruments, focus on a specific speaker, remove ambient noises, and many more amazing applications.Watch the video to learn more and hear some crazy results!
230. The Art of Data Creation: Behind the Scenes of AI Training
Keymakr's Head of Project Management, Dennis Sorokin, shares insights into the importance, process, challenges, and real-world applications of Data Creation.
231. Humans Go to War for Machines: A Case of Google and OpenAI
We thought nothing could beat the value that Google brings to the market, as a search engine. We had no idea we were in for a surprise.
232. Toon Filters And Video Transformation in EbSynth [Part 2]
233. The Ancient Secrets Hidden Inside Your LLM
A quick look at how today’s large language models trace back to ancient philosophy and why they rely on probability rather than true understanding.
234. What is an Artificial Neural Network (ANN)?
Artificial neural networks mimic the functioning of neurons in the human brain. They can learn from their original training and future runs.
235. The Noonification: Tapswap: What Is Everyone Tapping? (6/16/2024)
6/16/2024: Top 5 stories on the HackerNoon homepage!
236. Top 20 ML Stories For Data Science
Data Science is undoubtedly one of the main fields that every AI, ML, or data science enthusiast crosses paths with. Now with the advancement of data science, it is not just restricted to refine the data and then put it on the board. It is combined with Machine Learning that makes your machines smart by using the data that you just optimized to feed the machine.
237. Neural Networks and Deep Learning
Before you can code neural networks in any language or toolkit, first, you must understand what they are.
238. Build your Dataset from COCO with the Universal Data Tool
If you haven’t heard of the Universal Data Tool yet, it’s an open-source web or desktop program to collaborate, build and edit text, image, video, and audio datasets with labels and annotations.
239. Data Labeling for AI Products: How to Process Thousands of Data Labels
Here are a handful of recent case studies that show the power of data labeling in action.
240. Answering Whither Artificial Intelligence By Building A Bot
During one of our call with Yardy, discussing our next venture, we thought about implementing AI to streamline certain functions. Given that I had some experience with Machine Learning, our fund had a project aiming to evaluate ICOs & Coins on specific criteria.
241. The Role of AI and ML in Enhancing The Ability Of Multiplying Wealth
Landing a good job is generally considered the purpose of education today.
242. Meet the Writer: Hacker Noon's Contributor Thomas Cherickal, Independent Theoretical Researcher
Meet the man behind the trending stories - a self-driven, self-motivated independent research scientist, working because it's a passion for me.
243. The AI Takeover: Disrupting the Skies
Omri Hurwitz is joined by Fetcherr's CEO, Roy Cohen, a trailblazer with extensive expertise in streamlining logistics and boosting global businesses.
244. Apple Avoids Mentioning Artificial Intelligence at WWDC 2023
Among the various announcements made at WWDC 2023, one interesting observation was Apple's conspicuous avoidance of the phrase "Artificial Intelligence" (AI).
245. Important Considerations for Pushing AI to the Edge
AI at the edge means that we’re simply moving at least portions of the process out of centralized data centers closer to where the data originates and where decisions are made in the physical world.
246. 5 Skills Every Successful MLOps Engineer Should Have
Discover the five key skills every successful MLOps Engineer should have. Elevate your MLOps career with these crucial insights.
247. How Do I Best Secure My IoT Devices?
One of the biggest concerns of IoT is managing the risks associated with a growing number of IoT devices.
248. Artificial Intelligence and the Future of Humans
Artificial Intelligence is in many ways reshaping our tools and human-based methods, from the medical field to everyday gadgets and entertainment, to outer space. Humans are relying on AI more and more every day.
249. Amazon and Disney Take Voice Assistance to Magical Heights with “Hey Disney”
Amazon & Disney teamed up to launch a new voice assistant that will be live soon in the United States.
250. Introducing NVIDIA's EditGAN: Alter Images Instantly via Quick Sketches
EditGAN allows you to control any feature from quick drafts, and it will only edit what you want keeping the rest of the image the same!
251. Staying Ahead of Cyber Threats: Innovations in Cybersecurity and Phishing Mitigation
To counter evolving cyber threats, cybersecurity has witnessed remarkable technological progress, aiming to keep pace with these malicious advances.
252. AI Rewind: A Year of Amazing Machine Learning Papers
A curated list of the latest breakthroughs in AI by release date with a clear video explanation, link to a more in-depth article, and code.
253. AI Hotspots Across the US: Where AI Pros Are Thriving
Whether you used GPS to get to work or added a recommended add-on item to your online shopping cart, AI has likely touched your life in one way or another this very day. But does the increasing presence of AI in our day-to-day actually benefit us in more than just adding convenience to our lives? For tech pros, the answer is likely yes.
254. Is AI a Trick or a Treat? - 5 Fears About Artificial Intelligence
In the best Halloween tradition, we look at a few popular fears about AI that are actually coming true.
255. The 2021 AI Rewind: HackerNoon Edition
A curated list of the latest breakthroughs in AI and Data Science by release date with a clear video explanation
256. Deploying Your First ML Model to Production? Here’s What You Need to Know
Building a production-ready ML solution requires more than just tinkering with algorithms, as data sourcing and handling can be a major challenge.
257. 🔮 Decoding 2020's Favourite Buzzword: "Machine Unlearning"
258. Classify Handwritten Digits using Deep learning with Tensorflow
259. AI Is Not Just an API Call: What iOS Engineers Learn the Hard Way in Production
AI in iOS is not a simple API call. Streaming responses, backgrounding, memory pressure, and SwiftUI lifecycle break naive integrations. This article explains w
260. Recommender Engines: AI on Steroids for E-commerce
When I start using any website offering content or goods, I check how well a recommender system works. Do you?
261. YouTube Online Meetup: Face Recognition using Python and OpenCV
Meet the experts in the field, and get a personal career counselling session for your successful future career. 👩💻
262. Machine Learning and the Cloud: What SMEs Need to Know
Intelligent Cloud – ML assimilation in the Cloud, offers various ML services to make it affordable for SMEs to build, test, and implement their algorithms.
263. What are the Relevant Updates in Modzy 2.0 Container Template
Modzy is an enterprise software platform equipped to manage and host machine learning models built in any programming language or framework – at scale.
264. An Intro to Transfer Learning & Retraining
In simple terms, transfer learning is a machine learning approach where a model that is already trained on a specific data set and developed for a specific task
265. Cloud Empowerment: How AI and ML Are Reshaping Healthcare's Financial Backbone
With a synergetic convergence of ML, AI, and cloud computing, healthcare's financial landscape is undergoing a seismic shift.
266. How to Benchmark the End-to-End Performance of Different I/O Solutions for Model Training
This blog demonstrates how to set up and benchmark the end-to-end performance of the model training process.
267. This CEO was Going to be a Consultant but Decided to Solve Mental Health Issues With AI Instead.
Sumondo was nominated as one of the best startups in Copenhagen, Denmark, in HackerNoon’s Startups of the Year. This is an interview with their Founder CEO.
268. Machine Learning for Predictive Maintenance: How It's Reinventing Asset Upkeep
In this blog post, we share our experience in AI software consulting, dig into the innards of predictive maintenance systems, and share success stories...
269. 3 Essential Concepts Data Scientists Should Learn From MLOps Engineers
Discover how to bridge the knowledge gap between data scientists and MLOps engineers with these three essential concepts.
270. 4 IaC Services For Your ML Infrastructure All MLOps Leaders Should Know
Here are 4 IaC services you can use to supercharge your ML infrastructure.
271. A Simple Introduction to Edge AI
Edge AI starts with edge computing. Also called edge processing, edge computing is a network technology that positions servers locally near devices. This helps to reduce system processing load and resolve data transmission delays. These processes are performed at the location where the sensor or device generates the data, also called the edge.
272. How To Become A Machine Learning Practitioner Fast
Learn machine learning fast in 2022.
273. Enabling Scientific Research and Analyses Through Automated ML
Startup of the year interview with Ioannis Tsamardinos, CEO and co-founder at JADBio.
274. How to Deal With Major Challenges in Machine Learning
275. How Predictive Maintenance Can Quietly Transform Factory Operations
Predictive maintenance is one of the most funded uses of AI across all heavy industry sectors, from transportation to manufacturing and beyond. This is due to both its potential to improve budgeting and strategizing and reduce costs by providing an overview of the machinery that needs to be replaced.
276. Top Benefits of Machine Learning in Mobile App Development
According to Allied Market Research, Machine Learning is growing at a CAGR of 39.0% from 2017-2023. In addition to this, the report suggests that Machine Learning as a service market will reach $5,537 million in 2023.
277. Introducing ML News
I know.
278. Reinforcement Learning: 'Practice Makes a Machine Perfect'
Reinforcement learning is the fastest growing branches of machine learning. Embark your RL journey by getting a soft introduction to reinforcement learning now.
279. Essential Guide to Clustering In Unsupervised Learning
Data in itself has no value, it actually finds its expression when it is processed right, for the right purpose using the right tools.
280. Ryan Dawson on Open Source Tools and MLOps — A Noonie Nom Interview
Ryan Dawson is a 3x Noonie Nominee and is a top Hacker Noon contributor in the Software Development story category. In this interview, Ryan shares what he's learned about the open source value chain, MLOps, and problem solving with tech vs. people, or ideas.
281. A Data Scientist's Guide to Simplistic Time-Series Models
Today, we want to consider almost trivially simple models. If your dataset is small, the subsequent ideas might be useful.
282. Game On, AI! Why the Future of Advanced Intelligence is Being Forged in Virtual Worlds
283. Price as a Product: Dynamic Pricing With ML That Increases Revenue
ML-driven dynamic pricing for e-com/e-grocery, tackling the complexity of thousands of SKUs, promotions, and cashback.
284. How to Deploy ETL and ML Pipelines in the Fastest, Cheapest and Most Flexible Way Possible
Cost-Efficient and flexible ETL and ML pipelines deployment with a no-code solution.
285. Artificial Intelligence and Online Privacy: Blessing and a Curse
Artificial Intelligence (AI) is a beautiful piece of technology made to seamlessly augment our everyday experience. It is widely utilized in everything starting from marketing to even traffic light moderation in cities like Pittsburg. However, swords have two edges and the AI is no different. There are a fair number of upsides as well as downsides that follow such technological advancements.
286. Why Data Science is a Team Sport?
Today, I am going to cover why I consider data science as a team sport?
287. Pushing AI to the Edge: Use Cases and What is Next [Part 2]
In Part One of this two-part Q&A series we highlighted some key considerations for edge AI deployments. In this installment, our questions turn to emerging use cases and key trends for the future.
288. The Ethics of Machine Learning: Understanding the Role of Developers and Designers
We know that the whole world is fascinated by the tools that are using Machine learning and deep learning algorithms and they are fun to use.
289. 2 Years In The Life Of AI, ML, DL And Java - Part II
A follow-up post on the back of the post two-years ago with the title "Two Years In The Life Of AI, ML, DL And Java"
290. Architecting a Thousand-Node Data Orchestration Platform to Accelerate Game AI Training at Tencent
Tencent has implemented a 1000-node Alluxio cluster and designed a scalable, robust, and performant architecture to accelerate the game AI training.
291. What Did AI Bring to Computer Vision?
In this video, I will openly share everything about deep nets for computer vision applications, their successes, and the limitations we have yet to address.
292. On the Relevance of Software Engineering for the Development of ML based Software Systems
We are studying the emerging discipline of Machine Learning Engineering by investigating best practices for developing software systems that include ML components. In this article, we share the research motivation and approach, some initial results, and an invitation to help us by taking our 7-minute online survey on ML Engineering best practices.
293. ML-Based Batch Estimation for Dynamic Pricing in Same-Day Delivery Services
Five years ago, same-day delivery felt like a luxury. Today, it’s a baseline expectation.
294. Splitting Hairs: Exploring the Interrelationship of Machine Learning and AI
Navigating the Nuances: The Relationship and Differences Between AI and Machine Learning
295. 7 Competition-Killing Ways To Use Machine Learning for Ecommerce Brands
Leave competitors in your ecommerce niche gasping for air with these machine learning tools that automate costs out and show you where your customers are hiding.
296. AI-driven Features with the Highest Potential for Enterprise Development
Enterprise players across all industries are eager for optimization and improvement of their business processes: administration, customer service, marketing, sales, recruiting, and others. Today AI-driven software can cover the most common Enterprise needs like data security, data processing, resource optimization, and brand awareness. Forrester has reported that AI is also able to improve customer service and quality of existing products, increase revenue streams, and customer lifetime value.
297. Can We Really Trust AI?
AI is a phrase thrown about a lot nowadays. But do you even know what it means and that you’ve probably used it many times before without even realizing it
298. Date and Time Values are a Mess - Here's Why
This time I want to slightly expose how messy the situation is with date and time values.
299. How To Run Text Categorization: All Tips and Tricks from 5 Kaggle Competitions
In this article, I will discuss some great tips and tricks to improve the performance of your structured data binary classification model. These tricks are obtained from solutions of some of Kaggle’s top tabular data competitions. Without much lag, let’s begin.
300. Simplifying Feature Engineering for Real-Time AI/ML
A demonstration of how "timelines" make expressing temporal queries on events, and, importantly, between events, not just easy, but intuitive.
301. The ICLR 2020 Conference: Reinforcement Learning Papers Demystified
Last week I had a pleasure to participate in the International Conference on Learning Representations (ICLR), an event dedicated to the research on all aspects of representation learning, commonly known as deep learning. The conference went virtual due to the coronavirus pandemic, and thanks to the huge effort of its organizers, the event attracted an even bigger audience than last year. Their goal was for the conference to be inclusive and interactive, and from my point of view, as an attendee, it was definitely the case!
302. NYU and Facebook Make MRI Scans 4x Faster by Using AI
MRI has never been an easy option for a lot of people in the past who have suffered for this test in their life. There would be a considerable amount of unsettling experience that a patient has to bear throughout the MRI time. The Claustrophobia-inducing tube has to be placed over your body, and you have to be at a still position for more than one hour. It’s easy to think about in words, but the experience tends to become harder. Simultaneously, medical hardware creaks, whirs, and thumps around your body, which is not a good feeling.
However, thanks to the Facebook AI and NYU experts who have realized these issues and came up with the suggestion of artificial intelligence. It will help the entire system complete tests at a four-times faster speed. It will help all the patients to experience the process for a more decrease time and go. Hence, the entire process will get quicker as compared to the older times.
The best part about this model is that it will pair with high and low-resolution MRI scans. Another good thing about the procedure is that it will use the same model to predict the finals MRI scans results just after putting the input data for a quarter. In this way, there is a considerable chance that the data will come efficiently and faster than ever before. It will help the patients stop feeling the hassles they have to experience in the past so that the diagnoses will arrive more quickly.
According to Nafissa Yakubova (the AI researcher at FAIR working on this project, this decision is making a revolution in the MRI field.
The neural network makes it possible for the medial scan to construct an abstract idea. Later, the training data would be examined. Afterward, the same data will make it possible for the machine to predict the final output.
If we explain this idea with an example, consider an architect who tends to design infrastructure for numerous banks in a year or two. Later, the architect will get fully-aware of how a bank will look like. So, when a new project comes, the architect will finish the work quickly by creating the final blueprint.
If we talk about the FastMRI team, they have been working on this issue for a long time. However, the endless efforts made it easier for them to say that they have come up with a reliable method. The radiologists tried both AI and traditional ways of scans.
At the same time, the experts are also worried about the errors that are evident in the process. So, they are considering experiments as an essential part of this process. There are various instances where humans have to manually check the output and make all the things evident to match the input correctly. Moreover, it’s worrying that the MRI scans could also may product incorrect outputs due to false predictions by the algorithm.
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303. AI, ML Increasingly Indispensable in the Fight Against Cybercrime
Artificial intelligence (AI) – a computer science term that describes machines and computers which mimic such cognitive human functions as learning and problem solving – and machine learning (ML) – the study of algorithms and statistical models that computers use to perform tasks without being given explicit instructions – are undisputedly two of the most transformative technologies to have been developed in recent years. Both have already impacted a whole range of sectors with the scope of their application getting wider and wider every year.
304. The Noonification: Transformers: Age of Attention (8/27/2024)
8/27/2024: Top 5 stories on the HackerNoon homepage!
305. The Paradox of Brilliance: Why Our Smartest AI Still “Bluffs” And How We Can Teach It True Humility
Are LLMs bluffing?
306. Fraud Anomaly Model: A Powerful ML Tool for Detecting Unusual Activity
Comprehensive and insightful approach to fraud detection using the Anomaly Model but also explains detailed the limitations of traditional fraud prevention
307. We Released Modern Google-level Speech-to-Text Models
Our models are on par with premium Google models and also really simple to use.
308. The Future of Consulting and Legal Relationships Powered by AI
The potential for AI to not only replace many current tasks of lawyers but also perform them automatically is real.
309. How a small R&D team achieved great results in the Kaggle competition without using ML algorithms
A few months ago, Navigine R&D team started participating in Indoor Location & Navigation competition from XYZ10 and Microsoft Research.
310. Exploring the Limitations of Machine Learning
A discussion on the limitations of machine learning.
311. Meet the Writer: Hacker Noon's Contributor Elay Romanov of Daiger
A conversation with the COO and partner of Daiger
312. Reviewing “OpenPose - Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields”
OpenPose is an open-source multi-person detection system supporting the body, hand, foot, and facial key points. The system uses a multi-stage CNN.
313. Introduction to Observability in ITOM and AIOps
Observability is a best practice implemented by AIOps, enabling automation and expanding visibility into the entire organizational ecosystem.
314. A New Approach to Solve I/O Challenges in the Machine Learning Pipeline
Training and caching data can be done in a transparent and distributed way to improve training performance and simplify data management.
315. Podcast - When Machine Learning Meets Privacy
This is the first episode of a podcast series on Machine Learning and Data privacy.
316. "Give Your Team the Space to Develop Ideas," Chief AI Scientist Arash Azhand
Some time ago I had a chance to interview a great artificial intelligence researcher and Chief AI Scientist in Lindera, Arash Azhand.
317. The HackerNoon Newsletter: Startups, Meet Your New Distribution Channel: AI (9/18/2025)
9/18/2025: Top 5 stories on the HackerNoon homepage!
318. The Noonification: Maximizing Potential In BNB Staking (6/23/2024)
6/23/2024: Top 5 stories on the HackerNoon homepage!
319. Ensuring Trustworthy AI in Sports Safety: A Case Study of Real-Time Helmet Collision Detection
A real-time AI pipeline for detecting helmet collisions in American football using computer vision and player tracking data.
320. Machine Learning 101: How And Where To Start For Absolute Beginners
This post covers all you will need for your Journey as a Beginner. All the Resources are provided with links. You just need Time and Your dedication.
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