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The complete math derivation plus a 150-line PyTorch implementation that matches torch.nn.MultiheadAttention's performance.
Visualizing exploding gradients and implementing three different clipping strategies with benchmarks.
Build both Word2Vec architectures from scratch, train them on real text, and visualize the learned embeddings.
Reduce model size by 4x with minimal accuracy loss. Complete implementation of INT8 quantization with CUDA kernels.
Speed up transformer inference by 5x using key-value caching. Includes cache eviction strategies for long contexts.
Complete transformer decoder implementation from tokenization to text generation. Trains in 30 minutes on a single GPU.
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