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1
Intro
2
Foundations of Machine Learning
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The Transformer Architecture
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Transformer Decoder Overview
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Inputs
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Input Embedding
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Masked Multi-Head Attention
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Positional Encoding
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Skip Connections and Layer Norm
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Feed-forward Layer
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Transformer hyperparameters and Why they work so well
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Notable LLM: BERT
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Notable LLM: T5
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Notable LLM: GPT
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Notable LLM: Chinchilla and Scaling Laws
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Notable LLM: LLaMA
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Why include code in LLM training data?
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Instruction Tuning
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Notable LLM: RETRO
Description:
Dive into a comprehensive 48-minute video lecture on the foundational concepts of large language models. Explore core machine learning principles, the Transformer architecture, and notable LLMs. Gain insights into pretraining dataset composition, including the importance of code in training data. Learn about key components such as input embedding, masked multi-head attention, positional encoding, and feed-forward layers. Discover why Transformers work so well and examine notable models like BERT, T5, GPT, Chinchilla, LLaMA, and RETRO. Understand the significance of scaling laws and instruction tuning in LLM development. Access accompanying slides and additional resources for a deeper understanding of this rapidly evolving field.

LLM Foundations - LLM Bootcamp

The Full Stack
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#Computer Science #Artificial Intelligence #Natural Language Processing (NLP) #LLM (Large Language Model) #Machine Learning #BERT #T5 #Deep Learning #Transformer Architecture #Positional Encoding
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Intro