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
Context Vectors from Attention Weights
Was this section helpful?
- Neural Machine Translation by Jointly Learning to Align and Translate, Dzmitry Bahdanau, Kyunghyun Cho, Yoshua Bengio, 2015 International Conference on Learning Representations (ICLR) DOI: 10.48550/arXiv.1409.0473 - This pioneering paper introduces the attention mechanism in sequence-to-sequence models, detailing how a context vector is created as a weighted sum of encoder hidden states to focus on relevant input parts.
- Attention Is All You Need, Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, Illia Polosukhin, 2017 Advances in Neural Information Processing Systems 30 (NeurIPS 2017) DOI: 10.48550/arXiv.1706.03762 - This foundational paper introduces the Transformer architecture, which entirely relies on attention mechanisms and explicitly defines the computation of context vectors via weighted sums of Value vectors.
- CS224N: Natural Language Processing with Deep Learning - Lecture 8: Attention and Transformers, Christopher Manning, John Hewitt, 2023 (Stanford University) - Provides clear and accessible lecture slides explaining attention, self-attention, and the formulation of context vectors within the Transformer architecture for an introductory audience.
- Speech and Language Processing (3rd ed. draft) - Chapter 9: Attention and Transformers, Daniel Jurafsky and James H. Martin, 2024 - A comprehensive textbook chapter offering a detailed explanation of attention mechanisms and Transformer models, including the mathematical definition and purpose of the context vector.