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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, Key, 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

Advanced Transformer Architecture

Prerequisites: Strong foundation in deep learning, sequence modeling (RNNs/LSTMs), and Python programming with libraries like PyTorch or TensorFlow required.

Level: Advanced

What You'll Learn

Self-Attention Mechanisms
Analyze the mathematical formulation and computational aspects of scaled dot-product attention.
Multi-Head Attention
Understand the rationale and implementation details of projecting queries, keys, and values into multiple subspaces.
Positional Encoding
Evaluate different methods for injecting sequence order information into the Transformer model.
Encoder-Decoder Stack
Dissect the complete Transformer architecture, including layer normalization and feed-forward sub-layers.
Architectural Variants
Compare and contrast different Transformer modifications (e.g., sparse attention, linear transformers).
Implementation Considerations
Implement core Transformer components and understand computational efficiency trade-offs.

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