Deep Learning Cookbook: Practical Recipes to Get Started Quickly [First edition] 9781491995846, 0800340435865, 149199584X, 9781491995792, 1491995793, 9781491995815, 1491995815
Deep learning doesn't have to be intimidating. Until recently, this machine-learning method required years of study
160 83 9MB
English Pages xv, 234 pages: illustrations Year 2018
Table of 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
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
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
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