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Deep Learning Cookbook : Practical Recipes to Get Started Quickly

Author: Douwe Osinga
Publisher: Sebastopol, CA : O'Reilly Media : O'Reilly Media, 2018.
Edition/Format:   eBook : Document : English : First editionView all editions and formats
Summary:

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  Read more...

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Genre/Form: Electronic books
Additional Physical Format: Print version:
(OCoLC)1004763844
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Douwe Osinga
ISBN: 9781491995815 1491995815 9781491995792 1491995793
OCLC Number: 1038796826
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 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 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
Responsibility: Douwe Osinga.

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