Long Short-Term Memory Networks With Python

Description

Preface This book was born out of one thought: If I had to get a machine learning practitioner proficient with LSTMs in two weeks (e.g. capable of applying LSTMs to their own sequence prediction projects), what would I teach? I had been researching and applying LSTMs for some time and wanted to write something on the topic, but struggled for months on how exactly to present it. The above question crystallized it for me and this whole book came together. The above motivating question for this book is clarifying. It means that the lessons that I teach are focused only on the topics that you need to know in order to understand (1) what LSTMs are, (2) why we need LSTMs and (3) how to develop LSTM Copyright Contents Preface I Introductions Welcome Who Is This Book For? About Your Outcomes How to Read This Book About the Book Structure About Lessons About LSTM Models About Prediction Problems About Python Code Examples About Further Reading About Getting Help Summary II Foundations What are LSTMs Sequence Prediction Problems Limitations of Multilayer Perceptrons Promise of Recurrent Neural Networks The Long Short-Term Memory Network Applications of LSTMs Limitations of LSTMs Further Reading Extensions Summary How to Train LSTMs Backpropagation Training Algorithm Unrolling Recurrent Neural Networks Backpropagation Through Time Truncated Backpropagation Through Time Configurations for Truncated BPTT Keras Implementation of TBPTT Further Reading Extensions Summary How to Prepare Data for LSTMs Prepare Numeric Data Prepare Categorical Data Prepare Sequences with Varied Lengths Sequence Prediction as Supervised Learning Further Reading Extensions Summary How to Develop LSTMs in Keras Define the Model Compile the Model Fit the Model Evaluate the Model Make Predictions on the Model LSTM State Management Examples of Preparing Data Further Reading Extensions Summary Models for Sequence Prediction Sequence Prediction Models for Sequence Prediction Mapping Applications to Models Cardinality from Time Steps (not Features!) Two Common Misunderstandings Further Reading Extensions Summary III Models How to Develop Vanilla LSTMs The Vanilla LSTM Echo Sequence Prediction Problem Define and Compile the Model Fit the Model Evaluate the Model Make Predictions With the Model Complete Example Further Reading Extensions Summary How to Develop Stacked LSTMs The Stacked LSTM Damped Sine Wave Prediction Problem Define and Compile the Model Fit the Model Evaluate the Model Make Predictions with the Model Complete Example Further Reading Extensions Summary How to Develop CNN LSTMs The CNN LSTM Moving Square Video Prediction Problem Define and Compile the Model Fit the Model Evaluate the Model Make Predictions With the Model Complete Example Further Reading Extensions Summary How to Develop Encoder-Decoder LSTMs Lesson Overview The Encoder-Decoder LSTM Addition Prediction Problem Define and Compile the Model Fit the Model Evaluate the Model Make Predictions with the Model Complete Example Further Reading Extensions Summary How to Develop Bidirectional LSTMs The Bidirectional LSTM Cumulative Sum Prediction Problem Define and Compile the Model Fit the Model Evaluate the Model Make Predictions with the Model Complete Example Further Reading Extensions Summary How to Develop Generative LSTMs The Generative LSTM Shape Generation Problem Define and Compile the Model Fit the Model Make Predictions with the Model Evaluate the Model Complete Example Further Reading Extensions Summary IV Advanced How to Diagnose and Tune LSTMs Evaluating LSTM Models Robustly Diagnosing Underfitting and Overfitting Tune Problem Framing Tune Model Structure Tune Learning Behavior Further Reading Extensions Summary How to Make Predictions with LSTMs Finalize a LSTM Model Save LSTM Models to File Make Predictions on New Data Further Reading Extensions Summary How to Update LSTM Models What About New Data? What Is LSTM Model Updating? 5-Step Process to Update LSTM Models Extensions Summary V Appendix Getting Help Official Keras Destinations Where to Get Help with Keras How to Ask Questions Contact the Author How to Setup a Workstation for Deep Learning Overview Download Anaconda Install Anaconda Start and Update Anaconda Install Deep Learning Libraries Further Reading Summary How to Use Deep Learning in the Cloud Project Overview Setup Your AWS Account Launch Your Server Instance Login, Configure and Run Build and Run Models on AWS Close Your EC2 Instance Tips and Tricks for Using Keras on AWS Further Reading Summary VI Conclusions How Far You Have Come