HardikVala

I constantly find myself having to print raw tensors (either by hand or dumping them to stdout), especially when reading pytorch / jax code, to understand the transformations, e.g. for something like,

`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?

Considering the server version of llama.cpp for a commercial use case over bulkier options like vllm. But wondering if it's been battle-tested in production environments.

A variant of this question seems to get asked every 6 mo. but so far, I haven't seen this question tackled directly: If I want to learn the concepts and fundamentals of AI from first principles, what educational resources should I use?

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.