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
Quiz for Chapter 1
Chapter 2: Recurrent Neural Network Fundamentals
The Core Idea: Processing Sequences Iteratively
Simple RNN Architecture
The Role of the Hidden State
Mathematical Formulation of an RNN Cell
Information Flow in RNNs
Backpropagation Through Time (BPTT)
Unrolling the Network for Training
Quiz for Chapter 2
Chapter 3: Building Simple RNNs
Setting Up the Development Environment
RNN Cell Implementation
Using Framework APIs for Simple RNN Layers
Handling Input and Output Shapes
Constructing a Basic RNN Model
Training Loop for RNNs
Hands-on Practical: Simple Sequence Prediction
Quiz for Chapter 3
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
Quiz for Chapter 4
Chapter 5: Long Short-Term Memory (LSTM) Networks
Addressing RNN Limitations with Gating
The LSTM Cell Architecture
The Forget Gate
The Input Gate
Updating the Cell State
The Output Gate
Information Flow Through an LSTM Cell
Advantages of LSTMs
Quiz for Chapter 5
Chapter 6: Gated Recurrent Units (GRUs)
Introducing GRUs: A Simpler Gated Architecture
The GRU Cell Architecture
The Update Gate
The Reset Gate
Calculating the Candidate Hidden State
Calculating the Final Hidden State
Comparing GRU and LSTM
Computational Efficiency Considerations
When to Choose GRU or LSTM
Quiz for Chapter 6
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
Quiz for Chapter 7
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
Padding Sequences
Masking Padded Values
Batching Sequential Data
Preprocessing Time Series Data
Practice: Data Preparation Pipeline
Quiz for Chapter 8
Chapter 9: Sequence Modeling Application Techniques
Sequence Prediction Approaches
Time Series Forecasting Models
Sequence Classification Techniques
Text Classification Models
Sequence Generation Methods
Text Generation Models
Introduction to Encoder-Decoder Architecture
Brief Overview of Attention Mechanisms
Hands-on Practical: Time Series Forecasting
Quiz for Chapter 9
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
Quiz for Chapter 10
Troubleshooting Common Training Issues
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- Long Short-Term Memory, Sepp Hochreiter, Jürgen Schmidhuber, 1997 Neural Computation, Vol. 9 (The MIT Press) DOI: 10.1162/neco.1997.9.8.1735 - Introduces Long Short-Term Memory (LSTM) networks, a model designed to address gradient problems in recurrent neural networks and learn long-term dependencies.
- Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation, Kyunghyun Cho, Bart van Merriënboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio, 2014 Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) (Association for Computational Linguistics) DOI: 10.3115/v1/D14-1179 - Presents the Gated Recurrent Unit (GRU), an alternative to LSTMs that achieves comparable performance with fewer parameters.
- Deep Learning, Ian Goodfellow, Yoshua Bengio, and Aaron Courville, 2016 (MIT Press) - A comprehensive textbook on deep learning fundamentals, including optimization, regularization, and training methods for neural networks.
- CS231n Convolutional Neural Networks for Visual Recognition: Neural Network Training, Stanford University, 2023 (Stanford University) - Provides practical guidance on training deep neural networks, covering initialization, optimization, and regularization techniques applicable to various model architectures.