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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

Summary

In this chapter, we covered in detail the key features and concepts of TensorFlow. We started with discussing TensorFlow's main features and advantages, and key TensorFlow concepts such as ranks and tensors. We then looked at TensorFlow's computation graphs, and discussed how to launch a graph in a session environment, and you learned about placeholders and variables. We then saw different ways to evaluate tensors and execute operators, using Python variables or by referring to them via their name in the graph.

We went further to explore some of the essential TensorFlow operators and functions for transforming tensors, such as tf.transpose, tf.reshape, tf.split, and tf.concat. Finally, we saw how to visualize a TensorFlow computation graph using TensorBoard. Visualizing computation graphs using this module can be very useful, especially when we are debugging complex models.

In the next chapter, we'll make use of this library to implement an advanced image classifier: a Convolutional...