`x = torch.randn(32, 3, 224, 224).unfold(2, 16, 16).unfold(3, 16, 16).reshape(32, 3, 196, 256).transpose(1, 2).reshape(32, 196, 768).view(32, 196, 12, 64).transpose(1, 2)`
How do folks visualize tensors to quickly understand data flow in complex NN's?
I'm not interested in hands-on guides (eg. how to train a DNN classifier in TensorFlow) or LLM-centric resources.
So far, I've put together the following curriculum:
1 Artificial Intelligence: A Modern Approach (https://aima.cs.berkeley.edu/) - Great for learning the breadth of foundational concepts, eg. local search algorithms, building up to modern AI.
2 Probabilistic Machine Learning: An Introduction (https://probml.github.io/pml-book/book1.html) - Going more in-depth into ML.
3 Dive into Deep Learning (https://d2l.ai/) - Going deep into DL, including contemporary ideas like Transformers and Diffusion models.
4. Neural networks and Deep Learning (http://neuralnetworksanddeeplearning.com/) could also be a great resource but the content probably overlaps significantly with 3.
Would anybody add/update/remove anything? (Don't have to limit recommendations to textbooks. Also open to courses, papers, etc.)
Sorry for the semi-redundant post.