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Python Deep Learning

You're reading from   Python Deep Learning Next generation techniques to revolutionize computer vision, AI, speech and data analysis

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Product type Paperback
Published in Apr 2017
Publisher Packt
ISBN-13 9781786464453
Length 406 pages
Edition 1st Edition
Languages
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Authors (4):
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Peter Roelants Peter Roelants
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Peter Roelants
Daniel Slater Daniel Slater
Author Profile Icon Daniel Slater
Daniel Slater
Valentino Zocca Valentino Zocca
Author Profile Icon Valentino Zocca
Valentino Zocca
Gianmario Spacagna Gianmario Spacagna
Author Profile Icon Gianmario Spacagna
Gianmario Spacagna
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Toc

Table of Contents (12) Chapters Close

Preface 1. Machine Learning – An Introduction FREE CHAPTER 2. Neural Networks 3. Deep Learning Fundamentals 4. Unsupervised Feature Learning 5. Image Recognition 6. Recurrent Neural Networks and Language Models 7. Deep Learning for Board Games 8. Deep Learning for Computer Games 9. Anomaly Detection 10. Building a Production-Ready Intrusion Detection System Index

Conventions

In this book, you will find a number of text styles that distinguish between different kinds of information. Here are some examples of these styles and an explanation of their meaning.

Code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles are shown as follows: The code above for drawing should be immediately clear, we just notice that the line importing cm.

A block of code is set as follows:

(X_train, Y_train), (X_test, Y_test) = cifar10.load_data()
X_train = X_train.reshape(50000, 3072)
X_test = X_test.reshape(10000, 3072)
input_size = 3072

When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:

def monte_carlo_tree_search_uct(board_state, side, number_of_rollouts):
    state_results = collections.defaultdict(float)
    state_samples = collections.defaultdict(float)

Any command-line input or output is written as follows:

git clone https://github.com/fchollet/keras.git
cd keras
python setup.py install

New terms and important words are shown in bold.

Note

Warnings or important notes appear in a box like this.

Tip

Tips and tricks appear like this.

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