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
Loss Functions for Sequence Tasks
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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 (NIPS 2017) DOI: 10.48550/arXiv.1706.03762 - The foundational paper introducing the Transformer model, detailing its architecture, training objectives including cross-entropy loss, and the use of label smoothing.
- Deep Learning, Ian Goodfellow, Yoshua Bengio, and Aaron Courville, 2016 (MIT Press) - An authoritative textbook providing a comprehensive explanation of cross-entropy loss, its mathematical underpinnings, and its application in various deep learning models.
- torch.nn.CrossEntropyLoss, PyTorch Contributors, 2023 (PyTorch Foundation) - Official documentation for PyTorch's implementation of cross-entropy loss, detailing parameters like
ignore_indexfor handling padding in sequence tasks.