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LLM Foundations (LLM Bootcamp)
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New course announcement ✨
We're teaching an in-person LLM bootcamp in the SF Bay Area on November 14, 2023. Come join us if you want to see the most up-to-date materials building LLM-powered products and learn in a hands-on environment.
Hope to see some of you there!
--------------------------------------------------------------------------------------------- In this video, Sergey covers the foundational ideas for large language models: core ML, the Transformer architecture, notable LLMs, and pretraining dataset composition.
00:00 Intro
00:47 Foundations of Machine Learning
12:11 The Transformer Architecture
12:57 Transformer Decoder Overview
14:27 Inputs
15:29 Input Embedding
16:51 Masked Multi-Head Attention
24:26 Positional Encoding
25:32 Skip Connections and Layer Norm
27:05 Feed-forward Layer
27:43 Transformer hyperparameters and Why they work so well
31:06 Notable LLM: BERT
32:28 Notable LLM: T5
34:29 Notable LLM: GPT
38:18 Notable LLM: Chinchilla and Scaling Laws
40:23 Notable LLM: LLaMA
41:18 Why include code in LLM training data?
42:07 Instruction Tuning
46:34 Notable LLM: RETRO
We're teaching an in-person LLM bootcamp in the SF Bay Area on November 14, 2023. Come join us if you want to see the most up-to-date materials building LLM-powered products and learn in a hands-on environment.
Hope to see some of you there!
--------------------------------------------------------------------------------------------- In this video, Sergey covers the foundational ideas for large language models: core ML, the Transformer architecture, notable LLMs, and pretraining dataset composition.
00:00 Intro
00:47 Foundations of Machine Learning
12:11 The Transformer Architecture
12:57 Transformer Decoder Overview
14:27 Inputs
15:29 Input Embedding
16:51 Masked Multi-Head Attention
24:26 Positional Encoding
25:32 Skip Connections and Layer Norm
27:05 Feed-forward Layer
27:43 Transformer hyperparameters and Why they work so well
31:06 Notable LLM: BERT
32:28 Notable LLM: T5
34:29 Notable LLM: GPT
38:18 Notable LLM: Chinchilla and Scaling Laws
40:23 Notable LLM: LLaMA
41:18 Why include code in LLM training data?
42:07 Instruction Tuning
46:34 Notable LLM: RETRO
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