Pytorch

This one's for serious deep learning work in PyTorch, covering everything from custom nn.Module architectures to production-level concerns like mixed precision training and multi-GPU setups. It's opinionated about Hugging Face Transformers and Diffusers as your go-to libraries, which makes sense given how dominant they are. The focus on both transformers and diffusion models feels very 2024. What's nice is it doesn't just cover model building but actually addresses the unglamorous stuff like gradient clipping, handling NaN values, and proper data loading that separates research code from code that actually trains. If you're doing anything beyond basic PyTorch tutorials, this gives you reasonable guardrails.