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Python Machine Learning, Second Edition

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):

Sebastian Raschka

Vahid Mirjalili

View More author details

Table of Contents (24) Chapters

Python Machine Learning Second Edition

Credits

About the Authors

About the Reviewers

www.PacktPub.com

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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

Chapter 14. Going Deeper – The Mechanics of TensorFlow

In Chapter 13, Parallelizing Neural Network Training with TensorFlow, we trained a multilayer perceptron to classify MNIST digits, using various aspects of the TensorFlow Python API. That was a great way to dive us straight into some hands-on experience with TensorFlow neural network training and machine learning.

In this chapter, we'll now shift our focus squarely on to TensorFlow itself, and explore in detail the impressive mechanics and features that TensorFlow offers:

Key features and advantages of TensorFlow
TensorFlow ranks and tensors
Understanding and working with TensorFlow graphs
Working with TensorFlow variables
TensorFlow operations with different scopes
Common tensor transformations: working with ranks, shapes, and types
Transforming tensors as multidimensional arrays
Saving and restoring a model in TensorFlow
Visualizing neural network graphs with TensorBoard

We'll stay hands-on in this chapter, of course, and implement graphs throughout...

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Authors (2)

Sebastian Raschka

Sebastian Raschka

Sebastian Raschka is an Assistant Professor of Statistics at the University of Wisconsin-Madison focusing on machine learning and deep learning research. As Lead AI Educator at Grid AI, Sebastian plans to continue following his passion for helping people get into machine learning and artificial intelligence.

See other products by Sebastian Raschka

Vahid Mirjalili

Vahid Mirjalili

Vahid Mirjalili is a deep learning researcher focusing on CV applications. Vahid received a Ph.D. degree in both Mechanical Engineering and Computer Science from Michigan State University.

See other products by Vahid Mirjalili

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