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Intro
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Classroom Contents
LLM Foundations - LLM Bootcamp
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- 1 Intro
- 2 Foundations of Machine Learning
- 3 The Transformer Architecture
- 4 Transformer Decoder Overview
- 5 Inputs
- 6 Input Embedding
- 7 Masked Multi-Head Attention
- 8 Positional Encoding
- 9 Skip Connections and Layer Norm
- 10 Feed-forward Layer
- 11 Transformer hyperparameters and Why they work so well
- 12 Notable LLM: BERT
- 13 Notable LLM: T5
- 14 Notable LLM: GPT
- 15 Notable LLM: Chinchilla and Scaling Laws
- 16 Notable LLM: LLaMA
- 17 Why include code in LLM training data?
- 18 Instruction Tuning
- 19 Notable LLM: RETRO
