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
Benefits of Multiple Attention Heads
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- Attention Is All You Need, Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, and Illia Polosukhin, 2017 Advances in Neural Information Processing Systems 30, Vol. 30 (Curran Associates, Inc.) DOI: 10.48550/arXiv.1706.03762 - The foundational paper that introduced the Transformer architecture and Multi-Head Attention, explaining its design and the rationale for processing information from different representation subspaces.
- Dive into Deep Learning, Aston Zhang, Zachary C. Lipton, Mu Li, and Alex Smola, 2024 (Cambridge University Press) - An open-source educational resource that provides a clear, in-depth explanation of Multi-Head Attention, highlighting its role in allowing the model to focus on diverse aspects of the input simultaneously.
- Natural Language Processing with Transformers: Building Language Models with Hugging Face, Lewis Tunstall, Leandro von Werra, Thomas Wolf, 2022 (O'Reilly Media) - A comprehensive book offering practical insights into Transformers, including a detailed pedagogical discussion of Multi-Head Attention's architecture and the benefits of its multi-perspective approach to understanding sequence relationships.