You're reading from Python Machine Learning by Example Build intelligent systems using Python, TensorFlow 2, PyTorch, and scikit-learn

Product type Paperback

Published in Oct 2020

Publisher Packt

ISBN-13 9781800209718

Length 526 pages

Edition 3rd Edition

Languages

Python

Tools

PyTorch

Concepts

Machine Learning

Author (1):

Yuxi (Hayden) Liu

View More author details

Table of Contents (17) Chapters

Preface

1. Getting Started with Machine Learning and Python

2. Building a Movie Recommendation Engine with Naïve Bayes FREE CHAPTER

3. Recognizing Faces with Support Vector Machine

4. Predicting Online Ad Click-Through with Tree-Based Algorithms

5. Predicting Online Ad Click-Through with Logistic Regression

6. Scaling Up Prediction to Terabyte Click Logs

7. Predicting Stock Prices with Regression Algorithms

8. Predicting Stock Prices with Artificial Neural Networks

9. Mining the 20 Newsgroups Dataset with Text Analysis Techniques

10. Discovering Underlying Topics in the Newsgroups Dataset with Clustering and Topic Modeling

11. Machine Learning Best Practices

12. Categorizing Images of Clothing with Convolutional Neural Networks

13. Making Predictions with Sequences Using Recurrent Neural Networks

14. Making Decisions in Complex Environments with Reinforcement Learning

15. Other Books You May Enjoy

16. Index

Writing your own War and Peace with RNNs

In this project, we'll work on an interesting language modeling problem–text generation.

An RNN-based text generator can write anything, depending on what text we feed it. The training text can be from a novel such as A Game of Thrones, a poem from Shakespeare, or the movie scripts for The Matrix. The artificial text that's generated should read similar (but not identical) to the original one if the model is well-trained. In this section, we are going to write our own War and Peace with RNNs, a novel written by the Russian author Leo Tolstoy. Feel free to train your own RNNs on any of your favorite books.

We will start with data acquisition and analysis before constructing the training set. After that, we will build and train an RNN model for text generation.