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Material for The Hundred-Page Language Models Course

Andriy Burkov

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Artificial IntelligenceGPTTextbooksData ScienceComputer ScienceNeural NetworksDeep LearningLinguistics

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Author

About the Author

Andriy Burkov

Andriy Burkov holds a PhD in Artificial Intelligence. He works as a machine learning team leader at TalentNeuron.

Leanpub Podcast

Episode 319

An Interview with Andriy Burkov

Contents

Table of Contents

Lesson 1. Machine Learning Basics

  1. 1.1. About the videos
  2. 1.2. About this lesson
  3. 1.3. AI and Machine Learning
  4. 1.3.1. Early Progress
  5. 1.3.2. AI Winters
  6. 1.3.3. The Modern Era
  7. Exercise 1
  8. 1.4. Model
  9. Exercise 2
  10. 1.5. Four-Step Machine Learning Process
  11. 1.6. Vector
  12. Exercise 3
  13. 1.7. Neural Network
  14. Exercise 4
  15. 1.8. Matrix
  16. 1.9. Gradient Descent
  17. Exercise 5
  18. 1.10. Automatic Differentiation
  19. Exercise 6
  20. Quiz 1

Lesson 2. Language Modeling Basics

  1. 2.1. Bag of Words
  2. Exercise 7
  3. 2.2. Word Embeddings
  4. Exercise 8
  5. 2.3. Byte-Pair Encoding
  6. Exercise 9
  7. 2.4. Language Model
  8. 2.5. Count-Based Language Model
  9. 2.6. Evaluating Language Models
  10. 2.6.1. Perplexity
  11. 2.6.2. ROUGE
  12. 2.6.3. Human Evaluation
  13. Exercise 10
  14. Quiz 2

Lesson 3. Recurrent Neural Network

  1. 3.1. Elman RNN
  2. 3.2. Mini-Batch Gradient Descent
  3. Exercise 11
  4. 3.3. Programming an RNN
  5. 3.4. RNN as a Language Model
  6. Exercise 12
  7. 3.5. Embedding Layer
  8. 3.6. Training an RNN Language Model
  9. Exercise 13
  10. 3.7. Dataset and DataLoader
  11. Exercise 14
  12. 3.8. Training Data and Loss Computation
  13. Quiz 3

Lesson 4. Transformer

  1. 4.1. Decoder Block
  2. Exercise 15
  3. 4.2. Self-Attention
  4. 4.2.1. Step 1 of Self-Attention
  5. 4.2.2. Step 2 of Self-Attention
  6. 4.2.3. Step 3 of Self-Attention
  7. 4.2.4. Step 4 of Self-Attention
  8. 4.2.5. Step 5 of Self-Attention
  9. 4.2.6. Step 6 of Self-Attention
  10. 4.3. Position-Wise Multilayer Perceptron
  11. Exercise 16
  12. 4.4. Rotary Position Embedding
  13. Exercise 17
  14. 4.5. Multi-Head Attention
  15. Exercise 18
  16. 4.6. Residual Connection
  17. 4.7. Root Mean Square Normalization
  18. Exercise 19
  19. 4.8. Key-Value Caching
  20. 4.9. Transformer in Python
  21. Exercise 20
  22. Quiz 4

Lesson 5. Large Language Model

  1. 5.1. Why Larger Is Better
  2. 5.1.1. Large Parameter Count
  3. 5.1.2. Large Context Size
  4. 5.1.3. Large Training Dataset
  5. 5.1.4. Large Amount of Compute
  6. Exercise 21
  7. 5.2. Supervised Finetuning
  8. 5.3. Finetuning a Pretrained Model
  9. 5.3.1. Baseline Emotion Classifier
  10. 5.3.2. Emotion Generation
  11. 5.3.3. Finetuning to Follow Instructions
  12. Exercise 22
  13. 5.4. Sampling From Language Models
  14. 5.4.1. Basic Sampling with Temperature
  15. 5.4.2. Top-\mathbf{k} Sampling
  16. 5.4.3. Nucleus (Top-p) Sampling
  17. 5.4.4. Penalties
  18. 5.5. Low-Rank Adaptation (LoRA)
  19. 5.5.1. The Core Idea
  20. 5.5.2. Parameter-Efficient Finetuning (PEFT)
  21. 5.6. LLM as a Classifier
  22. Exercise 23
  23. 5.7. Prompt Engineering
  24. 5.7.1. Features of a Good Prompt
  25. 5.7.2. Follow-up Actions
  26. 5.7.3. Code Generation
  27. 5.7.4. Documentation Synchronization
  28. 5.8. Hallucinations
  29. 5.8.1. Reasons for Hallucinations
  30. 5.8.2. Preventing Hallucinations
  31. 5.9. LLMs, Copyright, and Ethics
  32. 5.9.1. Training Data
  33. 5.9.2. Generated Content
  34. 5.9.3. Open-Weight Models
  35. 5.9.4. Broader Ethical Considerations
  36. Quiz 5

Lesson 6. Further Reading

  1. 6.1. Mixture of Experts
  2. Exercise 24
  3. 6.2. Model Merging
  4. Exercise 25
  5. 6.3. Model Compression
  6. 6.4. Preference-Based Alignment
  7. Exercise 26
  8. 6.5. Advanced Reasoning
  9. 6.6. Language Model Security
  10. 6.7. Vision Language Model
  11. Exercise 27
  12. 6.8. Preventing Overfitting
  13. 6.9. Concluding Remarks
  14. 6.10. More From the Author
  15. Quiz 6

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