Autoencoders compress data manifold by forcing information through a bottleneck layer, learning efficient representations
Image: Courtesy NASA/JPL-Caltech., Public domain, via Wikimedia Commons
Autoencoders compress data manifold by forcing information through a bottleneck layer, learning efficient representations
Vector quantization
How can you store a huge library of books in a tiny closet?
Tensor network
Ever wondered how scientists manage massive data without endless storage?
weight tying does in language models: shares embedding and output projection matrices
Ever wonder how machines understand the sequence of words in a sentence?
batch size affects generalization: larger batches find sharper minima
Larger batch sizes lead to sharper minima, enhancing generalization by providing more accurate gradient estimates
the embedding layer does: maps discrete token IDs to dense learned vectors
Embeddings convert token IDs to dense vectors for neural network processing
quantization to INT8 doubles throughput
Quantization to INT8 doubles throughput because tensor cores process INT8 2x faster
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