Deep Learning Cookbook. Practical Recipes to Get Started Quickly

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• 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
  • OReilly Safari
  • How to Contact Us
  • Acknowledgments
• 1. Tools and Techniques
  • 1.1. Types of Neural Networks
  • 1.2. Acquiring Data
  • 1.3. Preprocessing Data
• 2. Getting Unstuck
  • 2.1. Determining That You Are Stuck
  • 2.2. Solving Runtime Errors
  • 2.3. Checking Intermediate Results
  • 2.4. Picking the Right Activation Function (for Your Final Layer)
  • 2.5. Regularization and Dropout
  • 2.6. Network Structure, Batch Size, and Learning Rate
• 3. Calculating Text Similarity Using Word Embeddings
  • 3.1. Using Pretrained Word Embeddings to Find Word Similarity
  • 3.2. Word2vec Math
  • 3.3. Visualizing Word Embeddings
  • 3.4. Finding Entity Classes in Embeddings
  • 3.5. Calculating Semantic Distances Inside a Class
  • 3.6. Visualizing Country Data on a Map
• 4. Building a Recommender System Based on Outgoing Wikipedia Links
  • 4.1. Collecting the Data
  • 4.2. Training Movie Embeddings
  • 4.3. Building a Movie Recommender
  • 4.4. Predicting Simple Movie Properties
• 5. Generating Text in the Style of an Example Text
  • 5.1. Acquiring the Text of Public Domain Books
  • 5.2. Generating Shakespeare-Like Texts
  • 5.3. Writing Code Using RNNs
  • 5.4. Controlling the Temperature of the Output
  • 5.5. Visualizing Recurrent Network Activations
• 6. Question Matching
  • 6.1. Acquiring Data from Stack Exchange
  • 6.2. Exploring Data Using Pandas
  • 6.3. Using Keras to Featurize Text
  • 6.4. Building a Question/Answer Model
  • 6.5. Training a Model with Pandas
  • 6.6. Checking Similarities
• 7. Suggesting Emojis
  • 7.1. Building a Simple Sentiment Classifier
  • 7.2. Inspecting a Simple Classifier
  • 7.3. Using a Convolutional Network for Sentiment Analysis
  • 7.4. Collecting Twitter Data
  • 7.5. A Simple Emoji Predictor
  • 7.6. Dropout and Multiple Windows
  • 7.7. Building a Word-Level Model
  • 7.8. Constructing Your Own Embeddings
  • 7.9. Using a Recurrent Neural Network for Classification
  • 7.10. Visualizing (Dis)Agreement
  • 7.11. Combining Models
• 8. Sequence-to-Sequence Mapping
  • 8.1. Training a Simple Sequence-to-Sequence Model
  • 8.2. Extracting Dialogue from Texts
  • 8.3. Handling an Open Vocabulary
  • 8.4. Training a seq2seq Chatbot
• 9. Reusing a Pretrained Image Recognition Network
  • 9.1. Loading a Pretrained Network
  • 9.2. Preprocessing Images
  • 9.3. Running Inference on Images
  • 9.4. Using the Flickr API to Collect a Set of Labeled Images
  • 9.5. Building a Classifier That Can Tell Cats from Dogs
  • 9.6. Improving Search Results
  • 9.7. Retraining Image Recognition Networks
• 10. Building an Inverse Image Search Service
  • 10.1. Acquiring Images from Wikipedia
  • 10.2. Projecting Images into an N-Dimensional Space
  • 10.3. Finding Nearest Neighbors in High-Dimensional Spaces
  • 10.4. Exploring Local Neighborhoods in Embeddings
• 11. Detecting Multiple Images
  • 11.1. Detecting Multiple Images Using a Pretrained Classifier
  • 11.2. Using Faster RCNN for Object Detection
  • 11.3. Running Faster RCNN over Our Own Images
• 12. Image Style
  • 12.1. Visualizing CNN Activations
  • 12.2. Octaves and Scaling
  • 12.3. Visualizing What a Neural Network Almost Sees
  • 12.4. Capturing the Style of an Image
  • 12.5. Improving the Loss Function to Increase Image Coherence
  • 12.6. Transferring the Style to a Different Image
  • 12.7. Style Interpolation
• 13. Generating Images with Autoencoders
  • 13.1. Importing Drawings from Google Quick Draw
  • 13.2. Creating an Autoencoder for Images
  • 13.3. Visualizing Autoencoder Results
  • 13.4. Sampling Images from a Correct Distribution
  • 13.5. Visualizing a Variational Autoencoder Space
  • 13.6. Conditional Variational Autoencoders
• 14. Generating Icons Using Deep Nets
  • 14.1. Acquiring Icons for Training
  • 14.2. Converting the Icons to a Tensor Representation
  • 14.3. Using a Variational Autoencoder to Generate Icons
  • 14.4. Using Data Augmentation to Improve the Autoencoders Performance
  • 14.5. Building a Generative Adversarial Network
  • 14.6. Training Generative Adversarial Networks
  • 14.7. Showing the Icons the GAN Produces
  • 14.8. Encoding Icons as Drawing Instructions
  • 14.9. Training an RNN to Draw Icons
  • 14.10. Generating Icons Using an RNN
• 15. Music and Deep Learning
  • 15.1. Creating a Training Set for Music Classification
  • 15.2. Training a Music Genre Detector
  • 15.3. Visualizing Confusion
  • 15.4. Indexing Existing Music
  • 15.5. Setting Up Spotify API Access
  • 15.6. Collecting Playlists and Songs from Spotify
  • 15.7. Training a Music Recommender
  • 15.8. Recommending Songs Using a Word2vec Model
• 16. Productionizing Machine Learning Systems
  • 16.1. Using Scikit-Learns Nearest Neighbors for Embeddings
  • 16.2. Use Postgres to Store Embeddings
  • 16.3. Populating and Querying Embeddings Stored in Postgres
  • 16.4. Storing High-Dimensional Models in Postgres
  • 16.5. Writing Microservices in Python
  • 16.6. Deploying a Keras Model Using a Microservice
  • 16.7. Calling a Microservice from a Web Framework
  • 16.8. TensorFlow seq2seq models
  • 16.9. Running Deep Learning Models in the Browser
  • 16.10. Running a Keras Model Using TensorFlow Serving
  • 16.11. Using a Keras Model from iOS
• Index

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