My current challenge is to convert neural networks created with the Tensorflow library (as used by many of the people who write books and create online courses) to an equivalent network created with the PyTorch library (which I have decided to use for all my deep learning activities). It should be fairly straightforward, but already I’ve run into a problem that I can’t fix. It was a fairly simple network and my PyTorch version is giving nonsensical results.
So what to do? This is a pretty important issue, becasue there’s a book being published in December that should be a great resource. However I have another work by Yves Hilpisch and he uses Tensorflow. I expect he will do so in this new book as well. Also my trading focused courses from Quantra use Tensorflow for the neural networks. Theoretically I should be able to use either on my computer but I’ve had conflicts, especially with regard to using the GPU, which is pretty important in deep learning. Besides, it SHOULD be easy to convert one to the other.
So my plan of action is to make sure I am completely familiar with the PyTorch library. I think the best resource for this is from machinelearningmastery.com, an ebook called Deep Learning with PyTorch, by Adrian Tam. I have several other resources but I think this one is the best. After that I’ll spend time converting TF networks to PyTorch networks, trying to make that process as seamless as possible. Then back to the Quantra courses, especially the one on Reinforcement Learning which I have discussed a bit in recent posts. That should take me to December. Then I can get back to learning to trade ADA. Finally!