Focus on deep learning: an update on AlphaGo, a fun side project, and a review of deep learning in 2016. Plus: 38 amazing stats developments, amazing data viz projects, and building a linear regression function from scratch.
Welcome to 2017!
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Focus on: Deep Learning
Over the past few days, Google’s Deepmind machine-learning team secretively put its AlphaGo artificial intelligence system onto two Chinese online board-game platforms to test its skill in fast-paced games against several of the world’s best Go players. It won every game it played. Go has become the province of AI, and DeepMind further proves that GANs are an extremely promising approach.
This post feels completely trivial, and that’s exactly the point. Deep learning is becoming straightforward enough to deploy that it can now be used in an increasing number of problem domains, including trivial ones. The author used a bunch of off-the-shelf tools including Keras and TensorFlow. Expect deep learning to be a core component to college CS projects and startup hackathons in 2017.
Also: this post is just freaking cool.
This is the best summary of deep learning activity over the past year that I’ve read. It focuses on new techniques (reinforcement learning and adversarial networks), the trend towards openness in the commercial research space, the white-hot acquisition market, and the advances in chips.
Very readable; highly recommended if you don’t follow deep learning closely.
This week's best data science articles
While the title of this post says “non-comprehensive”, that’s only really true in the most technical sense. This is a massive list of (38!) awesome developments in the world of stats in 2016.
Assuming you like lists of awesome things, this is an absolute must read.
Words do an extremely poor job of summarizing this article: instead, click through and scroll through the projects. There is some really inspiring work being done in data viz today.
Running a regression is a commonplace task—you can do it via a function call it any statistical programming language (even Excel!). But do you really understand what that function is doing? I always feel far more confident using functions when I understand the details of their implementation, and I love this post for diving into the internals of lm(). By the end, you could implement linear regression from scratch.
Data viz of the week
The Rhythm of Food (click through for *much* more!)
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The internet's most useful data science articles. Curated with ❤️ by Tristan Handy.
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