Advanced Transformer Architecture
Chapter 1: Revisiting Sequence Modeling Limitations
Sequential Computation in Recurrent Networks
The Vanishing and Exploding Gradient Problems
Long Short-Term Memory (LSTM) Gating Mechanisms
Gated Recurrent Units (GRUs) Architecture
Challenges with Long-Range Dependencies
Parallelization Constraints in Recurrent Models
Chapter 2: The Attention Mechanism: Core Concepts
Motivation: Overcoming Fixed-Length Context Vectors
General Framework: Query, Value Abstraction
Mathematical Formulation of Dot-Product Attention
Scaled Dot-Product Attention
The Softmax Function for Attention Weights
Computational Aspects and Matrix Operations
Practice: Implementing Scaled Dot-Product Attention
Chapter 3: Multi-Head Self-Attention
Self-Attention: Queries, Keys, Values from the Same Source
Limitations of Single Attention Head
Introducing Multiple Attention Heads
Linear Projections for Q, K, V per Head
Parallel Attention Computations
Concatenation and Final Linear Projection
Analysis of What Different Heads Learn
Hands-on Practical: Building a Multi-Head Attention Layer
Chapter 4: Positional Encoding and Embedding Layer
The Need for Positional Information
Input Embedding Layer Transformation
Sinusoidal Positional Encoding: Formulation
Properties of Sinusoidal Encodings
Combining Embeddings and Positional Encodings
Alternative: Learned Positional Embeddings
Comparison: Sinusoidal vs. Learned Embeddings
Practice: Generating and Visualizing Positional Encodings
Chapter 5: Encoder and Decoder Stacks
Overall Transformer Architecture Overview
Encoder Layer Structure
Decoder Layer Structure
Masked Self-Attention in Decoders
Encoder-Decoder Cross-Attention
Position-wise Feed-Forward Networks (FFN)
Residual Connections (Add)
Layer Normalization (Norm)
Stacking Multiple Layers
Final Linear Layer and Softmax Output
Hands-on Practical: Constructing an Encoder Block
Chapter 6: Advanced Architectural Variants and Analysis
Computational Complexity of Self-Attention
Sparse Attention Mechanisms
Approximating Attention: Linear Transformers
Kernel-Based Attention Approximation (Performers)
Low-Rank Projection Methods (Linformer)
Transformer-XL: Segment-Level Recurrence
Relative Positional Encodings
Pre-Normalization vs Post-Normalization (Pre-LN vs Post-LN)
Scaling Laws for Neural Language Models
Parameter Efficiency and Sharing Techniques
Chapter 7: Implementation Details and Optimization
Choosing a Framework (PyTorch, TensorFlow, JAX)
Weight Initialization Strategies
Optimizers for Transformers (Adam, AdamW)
Learning Rate Scheduling (Warmup, Decay)
Regularization Techniques (Dropout, Label Smoothing)
Gradient Clipping
Mixed-Precision Training
Efficient Attention Implementations (FlashAttention)
Model Parallelism and Data Parallelism Strategies
Practice: Analyzing Attention Weight Distributions
Regularization Techniques (Dropout, Label Smoothing)
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- Dropout: A Simple Way to Prevent Neural Networks from Overfitting, Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, Ruslan Salakhutdinov, 2014 Journal of Machine Learning Research, Vol. 15 - Original paper introducing the Dropout regularization technique for neural networks.
- 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, Vol. 30 DOI: 10.48550/arXiv.1706.03762 - The foundational paper introducing the Transformer architecture, which details the application of Dropout and Label Smoothing.
- When Does Label Smoothing Help?, Rafael Müller, Simon Kornblith, Geoffrey Hinton, 2019 NeurIPS 2019 DOI: 10.48550/arXiv.1906.02629 - Provides an analysis of Label Smoothing Regularization, exploring its benefits for model calibration and generalization.
- Dropout (nn.Dropout), PyTorch Authors, 2024 (PyTorch) - Official documentation for the PyTorch implementation of the Dropout layer, including usage details.