Recurrent Neural Networks and Sequence Modeling
Chapter 1: Introduction to Sequential Data
Understanding Sequential Data
Characteristics of Sequence Data
The Need for Sequence Models
Representing Sequences Numerically
Common Sequence Modeling Tasks
Chapter 2: Recurrent Neural Network Fundamentals
The Core Idea: Processing Sequences Iteratively
The Role of the Hidden State
Mathematical Formulation of an RNN Cell
Backpropagation Through Time (BPTT)
Unrolling the Network for Training
Chapter 3: Building Simple RNNs
Setting Up the Development Environment
Using Framework APIs for Simple RNN Layers
Handling Input and Output Shapes
Constructing a Basic RNN Model
Hands-on Practical: Simple Sequence Prediction
Chapter 4: Challenges in Training RNNs
The Problem of Vanishing Gradients
The Problem of Exploding Gradients
Impact on Long-Range Dependency Learning
Gradient Clipping Explained
Weight Initialization Strategies
Activation Functions Considerations
Chapter 5: Long Short-Term Memory (LSTM) Networks
Addressing RNN Limitations with Gating
The LSTM Cell Architecture
Information Flow Through an LSTM Cell
Chapter 6: Gated Recurrent Units (GRUs)
Introducing GRUs: A Simpler Gated Architecture
The GRU Cell Architecture
Calculating the Candidate Hidden State
Calculating the Final Hidden State
Computational Efficiency Considerations
When to Choose GRU or LSTM
Chapter 7: Implementing LSTMs and GRUs
Using LSTM Layers in Deep Learning Frameworks
Using GRU Layers in Deep Learning Frameworks
Configuring LSTM/GRU Layer Parameters
Stacking Recurrent Layers
Understanding Bidirectional RNNs
Implementing Bidirectional Layers
Hands-on Practical: Sentiment Analysis
Chapter 8: Preparing Sequence Data for RNNs
Text Data Preprocessing Overview
Tokenization and Vocabulary Building
Integer Encoding Sequences
Introduction to Embedding Layers
Handling Variable Length Sequences
Preprocessing Time Series Data
Practice: Data Preparation Pipeline
Chapter 9: Sequence Modeling Application Techniques
Sequence Prediction Approaches
Time Series Forecasting Models
Sequence Classification Techniques
Text Classification Models
Sequence Generation Methods
Introduction to Encoder-Decoder Architecture
Brief Overview of Attention Mechanisms
Hands-on Practical: Time Series Forecasting
Chapter 10: Evaluating and Tuning Sequence Models
Metrics for Sequence Classification
Metrics for Sequence Prediction
Metrics for Sequence Generation
Visualizing Model Behavior
Hyperparameter Tuning Strategies
Regularization Techniques for RNNs
Troubleshooting Common Training Issues
Practice: Tuning an RNN Model
