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cs249r_book
Knowledge Tutorial

A chapter-by-chapter walkthrough of cs249r_book, generated from its source code and tutorial markdown.

12
Chapters
-
Subsystems
∞
Rabbit Holes
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System Architecture

How the pieces fit

cs249r_book is organized as connected concepts and components. Start broad, then drill down chapter by chapter.

⚙️
Module Introduction
Module Introduction
⚙️
Core Utilities
Core Utilities
⚙️
Layer Normalization
Layer Normalization
⚙️
Layer Normalization Tests
Layer Normalization Tests
⚙️
Multi-Layer Perceptron
Multi-Layer Perceptron
⚙️
MLP Tests
MLP Tests
⚙️
Transformer Block
Transformer Block
⚙️
Transformer Block Tests
Transformer Block Tests
⚙️
GPT Architecture
GPT Architecture
⚙️
GPT Tests
GPT Tests
⚙️
Systems Analysis
Systems Analysis
⚙️
Integration and Conclusion
Integration and Conclusion
cs249r_book — bash
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Repository Overview

Intro and Architecture Diagram

TinyTorch is an educational framework for building Machine Learning Systems from scratch, specifically focusing on the implementation of Transformer architectures. It enables users to construct, train, and profile GPT-style models, providing deep insights into the systems impact of attention mechanisms, computational complexity, and memory scaling.

Source Repository: https://github.com/harvard-edge/cs249r_book

flowchart TD A0["Module Introduction"] A1["Core Utilities"] A2["Layer Normalization"] A3["Layer Normalization Tests"] A4["Multi-Layer Perceptron"] A5["MLP Tests"] A6["Transformer Block"] A7["Transformer Block Tests"] A8["GPT Architecture"] A9["GPT Tests"] A10["Systems Analysis"] A11["Integration and Conclusion"] A0 -->|"Introduces concepts"| A8 A1 -->|"Provides tensor ops"| A4 A3 -->|"Validates"| A2 A5 -->|"Validates"| A4 A6 -->|"Integrates"| A2 A6 -->|"Integrates"| A4 A7 -->|"Validates"| A6 A8 -->|"Stacks"| A6 A8 -->|"Uses embeddings"| A1 A9 -->|"Validates"| A8 A10 -->|"Analyzes scaling"| A8 A11 -->|"Executes"| A9 A11 -->|"Summarizes findings"| A10
Tutorial Chapters

All 12 chapters

Follow sequentially or jump to any topic. Start with Module Introduction.

Ch.01 CORE
Module Introduction
Welcome to the Transformer Architecture module!
↗
Ch.02 CORE
Core Utilities
In the Module Introduction, we outlined our mission: to build a Generative Pre-trained Transformer (GPT) from scratch. We saw the big pictu…
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Ch.03 CORE
Layer Normalization
In Core Utilities, we laid the foundation by creating our configuration and masking tools. Now, it is time to start building the actual lay…
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Ch.04 CORE
Layer Normalization Tests
In the previous chapter, Layer Normalization, we built a crucial component to stabilize the numbers flowing through our network. We learned…
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Ch.05 CORE
Multi-Layer Perceptron
In the previous chapters, we established our Core Utilities and built the Layer Normalization component to keep our numbers stable. We even…
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Ch.06 CORE
MLP Tests
In the previous chapter, Multi-Layer Perceptron, we built the "thinking" engine of our Transformer. We designed a neural network that expan…
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Ch.07 CORE
Transformer Block
In the previous chapters, we built the individual organs of our AI body. We built the Layer Normalization to keep our blood pressure (math)…
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Ch.08 CORE
Transformer Block Tests
In the previous chapter, Transformer Block, we assembled the main engine of our GPT model. We combined the "Communication" layer (Attention…
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Ch.09 CORE
GPT Architecture
In the previous chapter, Transformer Block Tests, we verified our "engine"—the Transformer Block. We proved that it processes information c…
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Ch.10 CORE
GPT Tests
In the previous chapter, GPT Architecture, we achieved a massive milestone: we assembled the entire Generative Pre-trained Transformer. We…
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Ch.11 CORE
Systems Analysis
In the previous chapter, GPT Tests, we verified that our model works. It processes data, learns from it, and doesn't crash.
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Ch.12 CORE
Integration and Conclusion
Welcome to the finale!
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About This Project

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