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Attention

A mechanism that allows neural networks to focus on relevant parts of the input when producing output.

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Understanding microGPT Walk through Karpathy's 200-line GPT implementation — from autograd and tokenization to multi-head attention, training, and text generation.

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What Is a GPT? Understanding GPT / The Big Picture
A Bigram Language Model Understanding GPT / The Bigram Baseline
Training and Generating Text Understanding GPT / The Bigram Baseline
Averaging Past Context Understanding GPT / Self-Attention
Softmax and Weighted Aggregation Understanding GPT / Self-Attention
Queries, Keys, and Values Understanding GPT / Self-Attention
Multi-Head Attention Understanding GPT / Building the Transformer
Feedforward Networks and Residual Connections Understanding GPT / Building the Transformer
The Complete Picture Understanding Makemore / Building a WaveNet
What Is microGPT? Understanding microGPT / The Big Picture
Backpropagation Understanding microGPT / Automatic Differentiation
Embeddings: Tokens and Positions Understanding microGPT / The Transformer Architecture
RMSNorm: Stabilizing Activations Understanding microGPT / The Transformer Architecture
Multi-Head Attention Understanding microGPT / The Transformer Architecture
The Training Loop Understanding microGPT / Training
From Micrograd to PyTorch Understanding Micrograd / The Full Picture

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