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Book Cover
E-book
Author Osinga, Douwe, author

Title Deep Learning Cookbook : Practical Recipes to Get Started Quickly / Douwe Osinga
Edition First edition
Published Sebastopol, CA : O'Reilly Media : O'Reilly Media, 2018
Online access available from:
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Description 1 online resource (xv, 234 pages) : illustrations
Contents Intro; Copyright; Table of Contents; Preface; A Brief History of Deep Learning; Why Now?; What Do You Need to Know?; How This Book Is Structured; Conventions Used in This Book; Accompanying Code; O'Reilly Safari; How to Contact Us; Acknowledgments; Chapter 1. Tools and Techniques; 1.1 Types of Neural Networks; Fully Connected Networks; Convolutional Networks; Recurrent Networks; Adversarial Networks and Autoencoders; Conclusion; 1.2 Acquiring Data; Wikipedia; Wikidata; OpenStreetMap; Twitter; Project Gutenberg; Flickr; The Internet Archive; Crawling; Other Options; 1.3 Preprocessing Data
4.1 Collecting the Data; Problem; Solution; Discussion; 4.2 Training Movie Embeddings; Problem; Solution; Discussion; 4.3 Building a Movie Recommender; Problem; Solution; Discussion; 4.4 Predicting Simple Movie Properties; Problem; Solution; Discussion; Chapter 5. Generating Text in the Style of an Example Text; 5.1 Acquiring the Text of Public Domain Books; Problem; Solution; Discussion; 5.2 Generating Shakespeare-Like Texts; Problem; Solution; Discussion; 5.3 Writing Code Using RNNs; Problem; Solution; Discussion; 5.4 Controlling the Temperature of the Output; Problem; Solution; Discussion
5.5 Visualizing Recurrent Network Activations; Problem; Solution; Discussion; Chapter 6. Question Matching; 6.1 Acquiring Data from Stack Exchange; Problem; Solution; Discussion; 6.2 Exploring Data Using Pandas; Problem; Solution; Discussion; 6.3 Using Keras to Featurize Text; Problem; Solution; Discussion; 6.4 Building a Question/Answer Model; Problem; Solution; Discussion; 6.5 Training a Model with Pandas; Problem; Solution; 6.6 Checking Similarities; Problem; Solution; Discussion; Chapter 7. Suggesting Emojis; 7.1 Building a Simple Sentiment Classifier; Problem; Solution; Discussion
Getting a Balanced Training Set; Creating Data Batches; Training, Testing, and Validation Data; Preprocessing of Text; Preprocessing of Images; Conclusion; Chapter 2. Getting Unstuck; 2.1 Determining That You Are Stuck; Problem; Solution; Discussion; 2.2 Solving Runtime Errors; Problem; Solution; Discussion; 2.3 Checking Intermediate Results; Problem; Solution; Discussion; 2.4 Picking the Right Activation Function (for Your Final Layer); Problem; Solution; Discussion; 2.5 Regularization and Dropout; Problem; Solution; Discussion; 2.6 Network Structure, Batch Size, and Learning Rate; Problem
Solution; Discussion; Chapter 3. Calculating Text Similarity Using Word Embeddings; 3.1 Using Pretrained Word Embeddings to Find Word Similarity; Problem; Solution; Discussion; 3.2 Word2vec Math; Problem; Solution; Discussion; 3.3 Visualizing Word Embeddings; Problem; Solution; Discussion; 3.4 Finding Entity Classes in Embeddings; Problem; Solution; Discussion; 3.5 Calculating Semantic Distances Inside a Class; Problem; Solution; Discussion; 3.6 Visualizing Country Data on a Map; Problem; Solution; Discussion; Chapter 4. Building a Recommender System Based on Outgoing Wikipedia Links
Summary Deep learning doesn't have to be intimidating. Until recently, this machine-learning method required years of study, but with frameworks such as Keras and Tensorflow, software engineers without a background in machine learning can quickly enter the field. With the recipes in this cookbook, you'll learn how to solve deep-learning problems for classifying and generating text, images, and music. Each chapter consists of several recipes needed to complete a single project, such as training a music recommending system. Author Douwe Osinga also provides a chapter with half a dozen techniques to help you if you're stuck. Examples are written in Python with code available on GitHub as a set of Python notebooks
Notes Online resource; title from PDF title page (EBSCO, viewed June 12, 2018)
Subject Machine learning.
Python (Computer program language)
Form Electronic book
ISBN 1491995793 (electronic bk.)
1491995815 (electronic bk.)
9781491995792 (electronic bk.)
9781491995815 (electronic bk.)