ML Course: Solution Articles for New Problems - #5696
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Add solution articles for multi-layer backpropagation, weight initialization, and batch normalization. These follow the same format as the existing 27 ML solution articles (Prerequisites, Concept, Solution with Python tabs, Common Pitfalls, In the GPT Project, Key Takeaways). Made-with: Cursor
- multi-headed-self-attention: Add output_proj (W_o) linear layer after concatenating heads, matching standard practice - transformer-block: Add output_proj to inner MultiHeadedSelfAttention class, consistent with multi-head attention problem - weight-initialization: Rewrite check_activations to use raw weight matrices (torch.randn * std) instead of nn.Linear for cross-platform determinism Made-with: Cursor
Reduces precision from 4 to 2 decimal places for the check_activations method to absorb cross-platform floating point differences in multi-layer matrix operations. Made-with: Cursor
- Remove softmax from solution, return raw logits - Add W_o output projection to inner MHA class - Update all explanatory text: probabilities → logits - Update shape table and key takeaways Made-with: Cursor
- rms-normalization.md — RMSNorm vs LayerNorm, numpy implementation, Llama/Mistral context - training-diagnostics.md — 3-method class (activation stats, gradient stats, diagnose), Karpathy debugging recipe Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Two new ML course problems (PR4), bringing total to 34. Dead ReLU Detector: detect_dead_neurons + suggest_fix with priority-ordered diagnostic rules. 8 test cases covering all fix branches. Tokenization Edge Cases: greedy tokenization exposing number inconsistency, token counting, and fertility scoring. 8 test cases. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Made-with: Cursor
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Summary
Replaces previously merged chained PRs (#5666, #5668, #5674, #5688, #5689) which merged into each other instead of main.
Test plan
Made with Cursor