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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

Chapter 2: Self-Attention and Multi-Head Attention

The Idea Behind Self-Attention

Query, Key, 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

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

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

Introduction to Transformer Models

Prerequisites: Basic Python programming, familiarity with machine learning concepts (vectors, neural networks), experience with a deep learning framework (PyTorch or TensorFlow).

Level: Intermediate

What You'll Learn

Attention Mechanisms
Explain the concept of attention and differentiate between various attention mechanisms.
Self-Attention
Describe how self-attention allows models to weigh the importance of different words in a sequence.
Transformer Architecture
Outline the components of the Transformer model, including encoder and decoder stacks.
Multi-Head Attention
Understand the rationale and implementation of multi-head attention.
Positional Encoding
Explain the necessity and methods for incorporating sequence order information.
Basic Implementation
Implement core components of the Transformer architecture using a deep learning framework.

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