Basic Autograd engine. - #9
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…tensor_loss - Clean up base_attention.py: remove dead _numpy_attention method and the one-line _tensor_attention wrapper, inlining the implementation directly into scaled_dot_product_attention() - Remove dead _tensor_loss method from losses/base.py - No functional changes — 379/379 tests pass
- Add docs/README.md — comprehensive documentation index mapping every component to its documentation and original research paper with arXiv links - Update README.md — add prominent 'Documentation' section linking to docs/README.md - Update 'Deep Dives' section with added Autograd Engine link
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/oc can you fix the tests ? |
…description The project now has a full autograd engine built from scratch in NumPy (Tensor, GradientTape, ops). Most layers use tape-based backward via base.Layer.backward() rather than manual backward methods.
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