Let's learn about Machine Learning Uses via these 53 free stories. They are ordered by most time reading created on HackerNoon. Visit the /Learn Repo to find the most read stories about any technology.
1. Use Cascade Models to Get Better Speed and Accuracy in Computer Vision Tasks
Great way to improve your Computer Vision models metrics
2. The Four Types of Machine Learning | Part 2
In the previous post, we saw the first two types of machine learning. In this post, we will discuss the other two types of machine learning. These are — Semi-su
3. The Convergence of AI + IOT is Imminent & Your Competitor is Already Preparing for It
With the emergence of ever-cheaper and robust hardware, 5G connectivity around the corner, and most importantly, a growing list of real world use cases, we can all agree that IOT projects are here to stay. But is that where it ends ?
4. 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.
5. Reimagining Customer Behavior Through Machine Learning
Here what you need to know about Machine Learning in business market strategy
6. Machine Learning: Santa’s little helper
7. How AI Is Transforming The Field Of Education
Artificial Intelligence (AI) is one of the wonders of the modern world, which isn’t going to cease to amaze the most intelligent of human beings. Like other fields, the field of education is also gaining the maximum amount of benefits out of the promises of the AI-powered technology.
8. Artificial Intelligence on Making Court Decisions
9. The GPUs for Deep Learning: NVIDIA vs AWS vs Azure and More
Take a deeper dive into what a GPU is, when you should use it or shouldn’t for Deep Learning tasks, and what is the best GPU on-premises and in the cloud in 202
10. 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.
11. 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.
12. How To Get Started With Machine Learning With The Right Mindset
You got intrigued by the machine learning world and wanted to get started as soon as possible, read all the articles, watched all the videos, but still isn’t sure about where to start, welcome to the club.
13. How Companies Are Actually Using AI in Everyday Practices
When thinking of AI (artificial intelligence), mixed emotions often come to mind. For movie buffs, we might immediately see images of Will Smith battling it out with humanoid AI creatures in IRobot or the even more realistic looking depiction of artificial intelligence in the movie aptly called AI. In our human minds, AI is something that could potentially lead to a catastrophic apocalypse as machines take over the world.
14. Every Way Natural Language is Better Than SQL
Since the dawn of time, humans have communicated through gestures, drawings, smoke, or speech. Along the way, Structured Query Language (SQL) made its way into human life so we could speak to databases. However, it’s time to revert back to our natural language and rethink how we talk to our data.
15. How Machine Learning Can Help With Fraud Prevention
Technology, without a doubt, has eased up a lot of issues for us, including the likes of fraud prevention. But before we start talking about the technological inputs pertaining to the same, it is necessary to understand why fraud prevention is actually required by businesses and why this genre of functionality is wide-spread and extremely popular. Firstly, financial firms are probably the most affected ones as fraudsters are actually interested in siphoning off money more than anything else. Secondly, fraudulent activities aren’t restricted to one vertical and it is a challenge for the firms to develop newer strategies for combating evolving threats.
16. Introduction to Different Machine Leaning Tools
Machine Learning is one of the emerging technologies of the present IT industry. This technology has now become the talk of the town and has seen an abnormally high growth over the few years.
17. 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.
18. Basic Use Cases of AI, ML, Deep Learning and Internet of Things
19. How is AI-powered Software Development influencing Pet Tech
Technology is revolutionizing all aspects of our lives. Let's take a look at how AI-powered software development influencing pet tech.
20. An Introduction to AI-Based Visual Inspection for Defect Detection
“Why should I care about a cool new technology until it’s solving any of my problems?” – this was the exact conversation I had with the executive of a water purification plant over a warm cup of coffee.
21. 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.
22. 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.
23. Vladimir Vapnik's New Learning Model
Vladimir Vapnik recently gave a talk about a new theory of learning he is working on.
24. Evaluating Regression Models in Machine Learning
Model evaluation is very important since we need to understand how well our model is performing.
25. Breaking into Deep Learning: Transforming the World Without Expert Input
Deep learning is a subdivision of machine learning in which Artificial Neural Networks (ANNs) learn from a huge influx of data to produce high-quality output.
26. Model Tests are Essential for Building Domain Knowledge
Testing protects against regressions. But the most important product of testing is domain knowledge and powerful capabilities.
27. Can Graph Neural Networks Solve Real-World Problems?
In this article, we will learn about GNNs and its structure as well as its applications
28. The Future of Artificial Intelligence: To Kill or To Heal?
The evil cyber-intelligence from the Matrix and a cyborg killing machine from the Terminator movies - that’s what most people used to imagine when talking about the future of artificial intelligence.
29. What’s the Difference Between AI And Machine Learning?
It’s nearly impossible to have a conversation about technology without mentioning artificial intelligence (AI) or machine learning (ML).
30. Here’s How Libratus, a Poker-Playing AI, Bluffed Four Professional Texas Holdem Players
The world of technology is changing at a faster rate than we can possibly fathom. Long gone are the days when we were the sole trailblazers in a human-tech relationship when the incentive resided in our hands.
31. What's the Difference Between MLOps and AIOps
An overview of the MLOps and AIOps worlds to understand what they mean, how they relate to DevOps, and how they compare in terms of benefits.
32. Machine Learning Applications Across Different Industries
1952 witnessed the world’s first computer that could learn while it was running. It was a game of checkers developed by Arthur Samuel. It has barely been half a century since then and we’re already having a conversation about whether we should commercialize self-driving cars or not. Machine Learning gave birth to some of these great advancements in technology and we’re going to dive deep into everything it can do to make our lives much easier than ever before.
33. How AI and Machine Learning Changes Modern-day Industries
It’s important to know exactly how AI and Machine Learning are transforming the industries and their role in our technological advancements.
34. Applications of Artificial Intelligence in Business
35. 8 Machine Learning Trends that Impact Business in 2021 and Beyond
Let’s discover the latest innovations in machine learning in 2021-2022 and go over various examples of how this technology can benefit you and your business.
36. Success With Machine Learning Projects in Python
In this article, we will give you a sense of the applications for machine learning and explain why Python is a perfect choice for getting started.
37. Build A Commission-Free Algo Trading Bot By Machine Learning Quarterly Earnings Reports [Full Guide]
38. What is Machine learning and Why is it Important?
Machine learning is quite an exciting field to study and rightly so. It is all around us in this modern world. From Facebook’s feed to Google Maps for navigation, machine learning finds its application in almost every aspect of our lives.
39. Is Automation Jeopardizing Our Future?
Is automation jeopardizing our future? What's its impact on our future and the job market? And how can we cope up?
40. Universal Data Tool Introduction: Weekly Update 2
If you haven’t heard of the Universal Data Tool, it’s an open-source web or desktop program to collaborate, build and edit text, image, video, and audio datasets with labels and annotations.
41. An Introduction to Variational Autoencoders Using Keras
In this tutorial, we’ll explore how Variational Autoencoders simply but powerfully extend their predecessors, ordinary Autoencoders.
42. How To Improve Data Quality When With Unsupervised Machine Learning
There won’t be any business insights if the data quality is poor.
43. How Machine Learning is Used in Astronomy
Is Astronomy data science?
44. Will Machine Learning Algorithms Encroach on Content Marketers?
Since its inception, the field of online content marketing has been in a constant state of flux. This tendency is built into it — since it operates in an ever changing online world, all it can ever do is change along with the internet. For better or for worse, the rapid advent of machine learning algorithms in online processes will only lead to more change in the field of content marketing.
45. How To Identify Trees with Deep Learning
Idea / inspiration
46. 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.
47. Embeddings in Machine Learning: Everything You Need to Know
Here's a deep dive into the history of machine learning embeddings, common uses, and current infrastructure solutions, including the vector database.
48. Quantum Machine Learning Using TensorFlow Quantum
49. 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?
50. Big Tech Companies Need Machine Learning Engineers
Machine learning is no longer a sci-fi concept, but an actual application of AI technology we use every day.
51. No Such Thing As The Intelligent Edge
But it will be much bigger than the Internet.
52. 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.
53. Optuna Vs. Hyperopt: Which Hyperparameter Optimization Library You Should Choose
Thinking which library should you choose for hyperparameter optimization?
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