Deep Learning Cookbook. Practical Recipes to Get Started Quickly

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Autor:: Douwe Osinga

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Deep Learning Cookbook. Practical Recipes to Get Started Quickly Douwe Osinga

Deep Learning. Receptury Douwe Osinga

Wnioskowanie przyczynowe w Pythonie. Praktyczne wykorzystanie w branży technologicznej Matheus Facure

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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.

You’ll learn how to:

Create applications that will serve real users
Use word embeddings to calculate text similarity
Build a movie recommender system based on Wikipedia links
Learn how AIs see the world by visualizing their internal state
Build a model to suggest emojis for pieces of text
Reuse pretrained networks to build an inverse image search service
Compare how GANs, autoencoders and LSTMs generate icons
Detect music styles and index song collections

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O autorze książki

Douwe Osinga jest doświadczonym inżynierem oprogramowania. Pochodzi z Haarlem w Holandii, do niedawna pracował dla Google w Zurychu, Hajdarabadzie i Sydney. Założył trzy startupy. Jest typowym globtroterem; zawodowo uwielbia wyzwania, które zmuszają go do łączenia technologii i wyników własnej pomysłowości.

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Deep Learning. Receptury

Autor:: Douwe Osinga

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Szczegóły książki

ISBN Ebooka:: 978-14-919-9579-2, 9781491995792
Data wydania ebooka :: 2018-06-05 Data wydania ebooka często jest dniem wprowadzenia tytułu do sprzedaży i może nie być równoznaczna z datą wydania książki papierowej. Dodatkowe informacje możesz znaleźć w darmowym fragmencie. Jeśli masz wątpliwości skontaktuj się z nami sklep@helion.pl.
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Spis treści książki

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