Introduction to Transformer Models
Chapter 1: Sequence Modeling and Attention Fundamentals
Challenges in Sequence-to-Sequence Tasks
Recap: Recurrent Neural Networks (RNNs)
Limitations of Traditional RNN Approaches
Introducing the Attention Mechanism Concept
Attention Score Calculation: A High-Level View
Context Vectors from Attention Weights
Quiz for Chapter 1
Chapter 2: Self-Attention and Multi-Head Attention
The Idea Behind Self-Attention
Query, and Value Vectors in Self-Attention
Scaled Dot-Product Attention Mechanism
Visualizing Self-Attention Scores
Introduction to Multi-Head Attention
How Multi-Head Attention Works
Benefits of Multiple Attention Heads
Hands-on Practical: Implementing Scaled Dot-Product Attention
Quiz for Chapter 2
Chapter 3: The Transformer Encoder-Decoder Architecture
Overall Architecture Overview
Input Embedding Layer
The Need for Positional Information
Positional Encoding Explained
The Encoder Stack
Add & Norm Layers (Residual Connections)
Position-wise Feed-Forward Networks
The Decoder Stack
Masked Multi-Head Self-Attention
Encoder-Decoder Attention Mechanism
Final Linear Layer and Softmax
Hands-on Practical: Building an Encoder Layer
Quiz for Chapter 3
Chapter 4: Training and Implementing Transformers
Data Preparation: Tokenization
Creating Input Batches
Loss Functions for Sequence Tasks
Optimization Strategies
Regularization Techniques
Overview of a Basic Implementation
Using Pre-trained Model Libraries (Brief)
Practice: Assembling a Basic Transformer
Quiz for Chapter 4
The Idea Behind Self-Attention
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- Attention Is All You Need, Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, Illia Polosukhin, 2017 Advances in Neural Information Processing Systems 30 (NIPS 2017) DOI: 10.48550/arXiv.1706.03762 - Introduces the Transformer architecture and the self-attention mechanism, forming the basis for many modern large language models.
- Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition, Daniel Jurafsky and James H. Martin, 2023 (Stanford University) - A comprehensive textbook providing detailed explanations of attention mechanisms and Transformer models in natural language processing.
- The Illustrated Transformer, Jay Alammar, 2018 - A highly visual and intuitive explanation of the Transformer model, particularly helpful for understanding the self-attention mechanism.
- Stanford CS224N: Natural Language Processing with Deep Learning - Transformers and Self-Attention, Christopher Manning, Abigail See, and Course Staff, 2024 (Stanford University) - Lecture slides from a leading university course, offering an academic perspective on the Transformer architecture and the self-attention mechanism.