You're reading from Python Machine Learning, Second Edition Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow

Product type Paperback

Published in Sep 2017

Publisher Packt

ISBN-13 9781787125933

Length 622 pages

Edition 2nd Edition

Languages

Python

Tools

Scikit-learn

Concepts

Deep Learning

Authors (2):

Vahid Mirjalili

Sebastian Raschka

View More author details

Table of Contents (18) Chapters

Preface

1. Giving Computers the Ability to Learn from Data FREE CHAPTER

2. Training Simple Machine Learning Algorithms for Classification

3. A Tour of Machine Learning Classifiers Using scikit-learn

4. Building Good Training Sets – Data Preprocessing

5. Compressing Data via Dimensionality Reduction

6. Learning Best Practices for Model Evaluation and Hyperparameter Tuning

7. Combining Different Models for Ensemble Learning

8. Applying Machine Learning to Sentiment Analysis

9. Embedding a Machine Learning Model into a Web Application

10. Predicting Continuous Target Variables with Regression Analysis

11. Working with Unlabeled Data – Clustering Analysis

12. Implementing a Multilayer Artificial Neural Network from Scratch

13. Parallelizing Neural Network Training with TensorFlow

14. Going Deeper – The Mechanics of TensorFlow

15. Classifying Images with Deep Convolutional Neural Networks

16. Modeling Sequential Data Using Recurrent Neural Networks

Index

Implementing a multilayer RNN for sequence modeling in TensorFlow

Now that we introduced the underlying theory behind RNNs, we are ready to move on to the more practical part to implement RNNs in TensorFlow. During the rest of this chapter, we will apply RNNs to two common problems tasks:

Sentiment analysis
Language modeling

These two projects, which we'll build together in the following pages, are both fascinating but also quite involved. Thus, instead of providing all the code all at once, we will break the implementation up into several steps and discuss the code in detail. If you like to have a big picture overview and see all the code at once before diving into the discussion, we recommend you to take a look at the code implementation first, which you can view at https://github.com/rasbt/python-machine-learning-book-2nd-edition/blob/master/code/ch16/ch16.ipynb.

Note, before we start coding in this chapter, that since we're using a very modern build of TensorFlow, we'll be...