Building an LLM from Scratch: A Deep Dive into Dropout

This post documents the author's journey through the dropout chapter of Sebastian Raschka's "Build a Large Language Model (from Scratch)". Dropout is a regularization technique that prevents overfitting by randomly ignoring some neurons or weights during training, thus spreading knowledge more broadly across the model. The author details the implementation of dropout and explores nuances of its application in LLMs, such as applying dropout to attention weights or value vectors, and rebalancing the resulting matrix. The post also touches upon practical dropout rate choices and the challenges of handling higher-order tensors for batch processing, setting the stage for further learning.
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