Systematically master the Transformer architecture that forms the foundation of modern NLP
Series Overview
This series is a practical educational content consisting of 5 chapters that allows you to learn the Transformer architecture systematically from the basics.
Transformer is the most revolutionary architecture in natural language processing (NLP) and forms the foundation of modern large language models (LLMs) such as BERT, GPT, and ChatGPT. By mastering parallel-processable sequence modeling through Self-Attention mechanism, learning diverse relationships through Multi-Head Attention, incorporating positional information through Positional Encoding, and transfer learning through pre-training and fine-tuning, you can understand and build state-of-the-art NLP systems. From the mechanisms of Self-Attention and Multi-Head to Transformer architecture, BERT/GPT, and large language models, we provide systematic knowledge.
Features:
- ✅ From Basics to Cutting Edge: Systematic learning from Attention mechanism to large-scale models like GPT-4
- ✅ Implementation-Focused: Over 40 executable PyTorch code examples and practical techniques
- ✅ Intuitive Understanding: Understand operational principles through Attention visualization and architecture diagrams
- ✅ Full Hugging Face Compliance: Latest implementation methods using industry-standard libraries
- ✅ Practical Applications: Application to practical tasks such as sentiment analysis, question answering, and text generation
Total Learning Time: 120-150 minutes (including code execution and exercises)
How to Learn
Recommended Learning Order
For Beginners (completely new to Transformer):
- Chapter 1 → Chapter 2 → Chapter 3 → Chapter 4 → Chapter 5 (all chapters recommended)
- Duration: 120-150 minutes
For Intermediate Learners (with RNN/Attention experience):
- Chapter 2 → Chapter 3 → Chapter 4 → Chapter 5
- Duration: 90-110 minutes
For Specific Topic Enhancement:
- Attention mechanism: Chapter 1 (focused study)
- BERT/GPT: Chapter 4 (focused study)
- LLM/Prompting: Chapter 5 (focused study)
- Duration: 25-30 minutes per chapter
Chapter Details
Chapter 1: Self-Attention and Multi-Head Attention
Difficulty: Intermediate
Reading Time: 25-30 minutes
Code Examples: 8
Learning Content
- Attention Fundamentals - Attention mechanism in RNN, alignment
- Self-Attention Principles - Query, Key, Value, similarity calculation by dot product
- Scaled Dot-Product Attention - Scaling, Softmax, weighted sum
- Multi-Head Attention - Multiple Attention heads, parallel processing
- Visualization and Implementation - PyTorch implementation, Attention map visualization
Learning Objectives
- ✅ Understand the operational principles of Self-Attention
- ✅ Explain the roles of Query, Key, and Value
- ✅ Calculate Scaled Dot-Product Attention
- ✅ Understand the benefits of Multi-Head Attention
- ✅ Implement Self-Attention in PyTorch
Chapter 2: Transformer Architecture
Difficulty: Intermediate to Advanced
Reading Time: 25-30 minutes
Code Examples: 8
Learning Content
- Overall Encoder-Decoder Structure - 6-layer stack, residual connections
- Positional Encoding - Positional information embedding, sin/cos functions
- Feed-Forward Network - Position-wise fully connected layers
- Layer Normalization - Normalization layer, training stabilization
- Masked Self-Attention - Masking future information in Decoder
Learning Objectives
- ✅ Understand the overall structure of Transformer
- ✅ Explain the role of Positional Encoding
- ✅ Understand the effects of residual connections and Layer Norm
- ✅ Explain the necessity of Masked Self-Attention
- ✅ Implement Transformer in PyTorch
Chapter 3: Pre-training and Fine-tuning
Difficulty: Intermediate to Advanced
Reading Time: 25-30 minutes
Code Examples: 8
Learning Content
- Transfer Learning Concept - Importance of pre-training, domain adaptation
- Pre-training Tasks - Masked Language Model, Next Sentence Prediction
- Fine-tuning Strategies - Full/partial layer updates, learning rate settings
- Data Efficiency - High performance with small data, Few-shot Learning
- Hugging Face Transformers - Practical library usage
Learning Objectives
- ✅ Understand the benefits of transfer learning
- ✅ Explain the design philosophy of pre-training tasks
- ✅ Select appropriate fine-tuning strategies
- ✅ Use the Hugging Face library
- ✅ Fine-tune models on custom tasks
Chapter 4: BERT and GPT
Difficulty: Advanced
Reading Time: 25-30 minutes
Code Examples: 8
Learning Content
- BERT Structure - Encoder-only, bidirectional context
- BERT Pre-training - Masked LM, Next Sentence Prediction
- GPT Structure - Decoder-only, autoregressive model
- GPT Pre-training - Language modeling, next token prediction
- Comparison of BERT and GPT - Task characteristics, selection criteria
Learning Objectives
- ✅ Understand BERT's bidirectionality
- ✅ Explain the learning mechanism of Masked LM
- ✅ Understand GPT's autoregressive nature
- ✅ Appropriately choose between BERT and GPT
- ✅ Implement sentiment analysis and question answering
Chapter 5: Large Language Models
Difficulty: Advanced
Reading Time: 30-35 minutes
Code Examples: 8
Learning Content
- Scaling Laws - Relationship between model size, data volume, and compute
- GPT-3 and GPT-4 - Ultra-large-scale models, Emergent Abilities
- Prompt Engineering - Few-shot, Chain-of-Thought
- In-Context Learning - Learning without fine-tuning
- Latest Trends - Instruction Tuning, RLHF, ChatGPT
Learning Objectives
- ✅ Understand scaling laws
- ✅ Explain the concept of Emergent Abilities
- ✅ Design effective prompts
- ✅ Utilize In-Context Learning
- ✅ Understand the latest LLM trends
Overall Learning Outcomes
Upon completing this series, you will acquire the following skills and knowledge:
Knowledge Level (Understanding)
- ✅ Explain the mechanisms of Self-Attention and Multi-Head Attention
- ✅ Understand the Transformer architecture
- ✅ Explain pre-training and fine-tuning strategies
- ✅ Understand the differences between BERT and GPT and how to use them
- ✅ Explain the principles and applications of large language models
Practical Skills (Doing)
- ✅ Implement Transformer in PyTorch
- ✅ Fine-tune using Hugging Face Transformers
- ✅ Implement sentiment analysis and question answering with BERT
- ✅ Implement text generation with GPT
- ✅ Design effective prompts
Application Ability (Applying)
- ✅ Select appropriate models for new NLP tasks
- ✅ Efficiently utilize pre-trained models
- ✅ Apply the latest LLM technologies to practical work
- ✅ Optimize performance through prompt engineering
Prerequisites
To effectively learn this series, it is desirable to have the following knowledge:
Required (Must Have)
- ✅ Python Basics: Variables, functions, classes, loops, conditionals
- ✅ NumPy Basics: Array operations, broadcasting, basic mathematical functions
- ✅ Deep Learning Fundamentals: Neural networks, backpropagation, gradient descent
- ✅ PyTorch Basics: Tensor operations, nn.Module, Dataset and DataLoader
- ✅ Linear Algebra Basics: Matrix operations, dot product, shape transformation
Recommended (Nice to Have)
- 💡 RNN/LSTM: Recurrent neural networks, Attention mechanism
- 💡 NLP Fundamentals: Tokenization, vocabulary, embeddings
- 💡 Optimization Algorithms: Adam, learning rate scheduling, Warmup
- 💡 GPU Environment: Basic understanding of CUDA
Recommended Prior Learning:
Technologies and Tools Used
Main Libraries
- PyTorch 2.0+ - Deep learning framework
- transformers 4.30+ - Hugging Face Transformers library
- tokenizers 0.13+ - Fast tokenizer
- datasets 2.12+ - Dataset library
- NumPy 1.24+ - Numerical computation
- Matplotlib 3.7+ - Visualization
- scikit-learn 1.3+ - Data preprocessing and evaluation metrics
Development Environment
- Python 3.8+ - Programming language
- Jupyter Notebook / Lab - Interactive development environment
- Google Colab - GPU environment (free to use)
- CUDA 11.8+ / cuDNN - GPU acceleration (recommended)
Datasets
- GLUE - Natural language understanding benchmark
- SQuAD - Question answering dataset
- WikiText - Language modeling dataset
- IMDb - Sentiment analysis dataset
Let's Get Started!
Are you ready? Begin with Chapter 1 and master Transformer technology!
Chapter 1: Self-Attention and Multi-Head Attention →
Next Steps
After completing this series, we recommend proceeding to the following topics:
Advanced Learning
- 📚 Vision Transformer (ViT): Transformer application to image processing
- 📚 Multimodal Learning: CLIP, Flamingo, GPT-4V
- 📚 Efficiency Techniques: Model compression, distillation, quantization
- 📚 Integration with Reinforcement Learning: RLHF, Constitutional AI
Related Series
- 🎯 NLP Advanced (Coming Soon) - Sentiment analysis, question answering, summarization
- 🎯 - RAG, agents, tool use
- 🎯 - Practical prompt design
Practical Projects
- 🚀 Sentiment Analysis API - Real-time sentiment analysis with BERT
- 🚀 Question Answering System - Document retrieval and answer generation
- 🚀 Chatbot - GPT-based dialogue system
- 🚀 Text Summarization Tool - Automatic news article summarization
Update History
- 2025-10-21: v1.0 Initial release
Your Transformer learning journey begins here!