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Our first editorial pick provides a high-level description of DeepMind’s approach that uses game theory to reimagine PCA. If you’d like to dive into the ICLR award-winning paper by DeepMind called “EigenGame: PCA as a Nash Equilibrium,” you can find it on OpenReview.
Now, let’s face it. AI systems are often opaque, strange, and challenging to use. In the field of machine learning, this is particularly true. If we want to make intelligent systems that people can understand and interact with — more efficiently, a crucial part of the solution is a community where people can come together, share ideas and learn from each other. That is why we created our AI community on Discord — to connect and learn with other data experts and enthusiasts.
If you are into highly technical and novel machine learning work. We recommend you to check out this talk by Snorkel AI on “Applying Information Theory to ML” with Fred Sala, a research scientist at Snorkel and assistant professor at the University of Wisconsin.
The Neural Information Processing Systems (NeurIPS) conference is doubling down on their efforts to be fully remote, and have come up with 2021 Meetups for this year, to learn more about it, please visit this post on how to host a NeurIPS 2021 Meetup.
If you have not checked it out yet, we recently launched our book on descriptive statistics with Python. This article or this PDF provides a sample of the first 36 pages of the book. Please don’t forget that you can access this work, many more books, and other goodies by becoming a member.
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Now onto the monthly picks! We pick these articles based on readers, fans, and views a specific piece gets. We hope you enjoy reading them as much as we did. Also, we started doing something new! We will pick our top-performing articles, and our editors will choose a couple of essays that didn’t have outstanding performance, but due to their quality — they made the cut for the month.
📚 Editor’s choice featured articles of the month ↓ 📚
Principal component analysis(PCA) is one of the key algorithms that are part of any machine learning curriculum. Initially created in the early 1900s, PCA is a fundamental algorithm to understand data in high-dimensional spaces, which are common in deep learning problems…
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