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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
by 2bitencryption 8y ago
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?
- adamnemecek 8y agoThe one advantage of pytorch is that you can make your graphs dynamic.
- 0101111101 8y agoTensorflow also created Eager - their dynamic environment
- joshvm 8y agoOnce 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/ https://blog.openai.com/spinning-up-in-deep-rl/
- jph00 8y agoI discussed this a bit here: https://www.fast.ai/2017/09/08/introducing-pytorch-for-fastai/ 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/ https://www.fast.ai/2018/10/02/fastai-ai/
- chewxy 8y agoIf you're interested in a Go version of the AlphaGo algorithm, I wrote one: http://github.com/gorgonia/agogo http://github.com/gorgonia/agogo :)
- z0k 8y agoYou 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-go https://www.manning.com/books/deep-learning-and-the-game-of-...