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
Creating Input Batches
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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 DOI: 10.48550/arXiv.1706.03762 - Describes the Transformer architecture, including the attention mechanism and the use of masks in both the encoder and decoder to manage sequence information.
- Preprocessing data, Hugging Face, 2024 - Provides practical guidance on tokenization, padding, and attention mask creation for Transformer models using the Hugging Face
transformerslibrary. - Speech and Language Processing (3rd ed. draft), Daniel Jurafsky and James H. Martin, 2023 (Stanford University) - A comprehensive textbook covering core NLP concepts, including data preparation techniques for sequence models and details on Transformer architectures, which implicitly rely on padding and masking.
- Writing Custom Datasets, DataLoaders and Transforms, PyTorch Team, 2024 - Explains the fundamental concepts of
DatasetandDataLoaderin PyTorch, which are essential for batching and custom padding logic in data preparation pipelines for deep learning models, including Transformers.
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