Welcome to a very special article in collaboration with my friends at Hackernoon, where I also share my videos. Here are the five best articles related to artificial intelligence in February, hoping they will make you want to learn more and visit their website. The five articles you will see were curated by myself amongst hundred of other super interesting ones that you might enjoy even more.
So please feel free to look at the AI tag on Hackernoon and keep learning! And please let me know what you think of this format by liking and commenting under my YouTube video or reach out by email if you'd like it to become a monthly thing or not! Personally, I loved going through all the articles and finding these five gems to share with you.
But first, I think you should know a bit more about Hackernoon and why I like them. HackerNoon is the best place for software developers, blockchain experts, data scientists, and tech people like us to read, write, and publish. Indeed, just like me, you can also publish your own work on there, for free! Giving you great visibility in your niche, and they will help spread your articles on Twitter and other media, which helped me quite a lot.
This first article is incredible.
It won the fifth position simply because it isn't really an article but a complete course. Pau Labarta Bajo did a fantastic job explaining reinforcement learning in this part 1 of his article series, aiming to cover the fundamentals up to cutting edge techniques used in reinforcement learning.
He walks you through a clear step-by-step course with coding examples and tutorials in Python! You have the theory, analogies, examples, code, jokes... He even provides Homeworks and his email address to reach out if needed.
It's beautifully explained and a pretty good resource if you'd like to get into reinforcement learning! And it is entirely free!
The fourth position goes to Bala Priya with her article called "Learn K-Means Clustering by Quantizing Color Images in Python".
In this article, Bala covers a wide range of subjects such as supervised learning vs. unsupervised learning, the K-means algorithm, the elbow method, and a clear example explaining these concepts using color quantization.
She's a fantastic writer and teacher and even comes back later in this top five! Having learned K-means with the same example, it's the best way to visualize and grasp how it works. She provides clear explanations of the math and code behind the algorithms, great visuals, and even code for you to implement it as well.
If you are not familiar with K-Means or color quantization, you should definitely read more. This algorithm is widely used and pretty powerful. You will love it! And it's pretty fun to play with the images you generate using the algorithm!
Yes, this is an article about Weights and Biases, and it isn't even sponsored! This is because I simply love their tool, and so does CodeChem, the authors of this article cover how to perform hyperparameter optimization.
I already speak a lot about Weights and Biases in my other videos so I won't enter into the details here, but this is a fantastic article if you'd like to learn how to improve your w&b skills and have a clear guide to set your optimization process up easily.
It has a clear code example, and a great explanation of hyperparameter optimization's different approaches and tools. Check this one out if you use w&b to track your experiments!
Confusion matrices are extremely useful for evaluating the performance of your machine learning models.
In this great article, Bala Priya once again provides a fantastic explanation. This time covers what a confusion matrix is, why to use it and when to do so. The whole tutorial is extremely clear with everything you need to follow along. She also includes an overview of machine learning and classification tasks.
As she highlights, "This tutorial will help you understand the confusion matrix and the various metrics that you can calculate from the confusion matrix.". So if you work with classification models or are starting your way in machine learning, this is a must-read!
And my favorite article of the month is...
In this article, Sarah Othman from the non-profit organization Verified Writers covers a side of GPT-3 that isn't discussed enough: the dangers of having access to such a great language model.
She covers what GPT-3 is, its risks, the dangers of fake news, and why a good AI may be dangerous for writers and the population. The latter is also the reason Open-AI used to explain why they licensed the usage of GPT-3 instead of open-sourcing it. I think there may be other reasons for that, but hey, who knows! (show money)
The article is interesting and well written, hopefully not by GPT-3! It even provides tools to, as they say, "address the rise of robots" and protect human writers
This last one is my favorite due to its discussion of AI ethics. If AI ethics also interests you, you should definitely read this piece.
Also, I'd strongly invite you to follow my newsletter, where two amazing people work with me to share opinions and knowledge about the ethical side of the papers I cover here on the channel. In this week's iteration, Martina Todaro extends on this last article with a very interesting view.
I hope you enjoyed this special article, and don't forget to subscribe to my newsletter learn more about AI, stay up to date with new research, and support my work!
Please let me know what you think of this format in the comments and leave a like under the video if you'd like it to become a monthly thing or not!
Before you leave, don't forget that you have until the end of March to participate in the NVIDIA RTX 3080Ti GPU giveaway in my previous video!
I will see you next week with another awesome paper covered!
Thank you for reading.
Hackernoon AI stories: https://hackernoon.com/tagged/AI
►The Article: https://www.louisbouchard.ai/hn-top-5-articles-february/ The Top 5: ►5: Reinforcement Learning Course: Part 1 - https://hackernoon.com/reinforcement-learning-course-part-1
►4: Learn K-Means Clustering by Quantizing Color Images in Python - https://hackernoon.com/learn-k-means-clustering-by-quantizing-color-images-in-python
►3: Using Weights and Biases to Perform Hyperparameter Optimization - https://hackernoon.com/using-weights-and-biases-to-perform-hyperparameter-optimization
►2: Confusion Matrix in Machine Learning: Everything You Need to Know - https://hackernoon.com/confusion-matrix-in-machine-learning-everything-you-need-to-know
►1: Verified Writers vs. GPT3: Combating Disinformation with the Rise of Robots - https://hackernoon.com/verified-writers-vs-gpt3-combating-disinformation-with-the-rise-of-robots
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