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Deep Learning Quick Reference

You're reading from Deep Learning Quick Reference Useful hacks for training and optimizing deep neural networks with TensorFlow and Keras

Product type Paperback

Published in Mar 2018

Publisher Packt

ISBN-13 9781788837996

Length 272 pages

Edition 1st Edition

Languages

Python

Tools

Keras

Concepts

Deep Learning

Author (1):

Mike Bernico

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Table of Contents (15) Chapters

Preface

1. The Building Blocks of Deep Learning FREE CHAPTER

2. Using Deep Learning to Solve Regression Problems

3. Monitoring Network Training Using TensorBoard

4. Using Deep Learning to Solve Binary Classification Problems

5. Using Keras to Solve Multiclass Classification Problems

6. Hyperparameter Optimization

7. Training a CNN from Scratch

8. Transfer Learning with Pretrained CNNs

9. Training an RNN from scratch

10. Training LSTMs with Word Embeddings from Scratch

11. Training Seq2Seq Models

12. Using Deep Reinforcement Learning

13. Generative Adversarial Networks

14. Other Books You May Enjoy

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Training Seq2Seq Models

In the last chapter, we talked about document classification, and a special case of document classification called sentiment classification. In doing so, we got to talk quite a bit about vectorization.

In this chapter, we're going to keep talking about solving NLP problems, but instead of classifying, we're going to generate new sequences of words.

We will cover the following topics in this chapter:

Sequence-to-sequence models
Machine translation

The rest of the chapter is locked

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Authors (1)

Mike Bernico

Mike Bernico

Mike Bernico is a Lead Data Scientist at State Farm Mutual Insurance Companies. He also works as an adjunct for the University of Illinois at Springfield, where he teaches Essentials of Data Science, and Advanced Neural Networks and Deep Learning. Mike earned his MSCS from the University of Illinois at Springfield. He's an advocate for open source software and the good it can bring to the world. As a lifelong learner with umpteen hobbies, Mike also enjoys cycling, travel photography, and wine making.

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