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I'm so excited to dive into this.

One question: is there a reason you opted for PyTorch over Keras? I had the impression that Keras was the go-to for "easy as ABC" neural networking.

EDIT: bonus question! I'm really fascinated with gameplay agents like AlphaGo and more recently AlphaStar. If I finish this course, will I have what I need to start work on a toy version of some gameplaying agent? If not, could you recommend where I could go next to start exploring that area?



I discussed this a bit here: https://www.fast.ai/2017/09/08/introducing-pytorch-for-fasta...

Also some info in the fastai release post: https://www.fast.ai/2018/10/02/fastai-ai/


Once you've done fast.ai, you can look at OpenAI's crash course in reinforcement learning: https://blog.openai.com/spinning-up-in-deep-rl/


If you're interested in a Go version of the AlphaGo algorithm, I wrote one: http://github.com/gorgonia/agogo :)


You can start with this book if you want to go straight to making a gameplay agent https://www.manning.com/books/deep-learning-and-the-game-of-...


The one advantage of pytorch is that you can make your graphs dynamic.


Tensorflow also created Eager - their dynamic environment




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