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Apache Spark Deep Learning Cookbook

You're reading from   Apache Spark Deep Learning Cookbook Over 80 best practice recipes for the distributed training and deployment of neural networks using Keras and TensorFlow

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Product type Paperback
Published in Jul 2018
Publisher Packt
ISBN-13 9781788474221
Length 474 pages
Edition 1st Edition
Languages
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Authors (2):
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Ahmed Sherif Ahmed Sherif
Author Profile Icon Ahmed Sherif
Ahmed Sherif
Amrith Ravindra Amrith Ravindra
Author Profile Icon Amrith Ravindra
Amrith Ravindra
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Toc

Table of Contents (15) Chapters Close

Preface 1. Setting Up Spark for Deep Learning Development 2. Creating a Neural Network in Spark FREE CHAPTER 3. Pain Points of Convolutional Neural Networks 4. Pain Points of Recurrent Neural Networks 5. Predicting Fire Department Calls with Spark ML 6. Using LSTMs in Generative Networks 7. Natural Language Processing with TF-IDF 8. Real Estate Value Prediction Using XGBoost 9. Predicting Apple Stock Market Cost with LSTM 10. Face Recognition Using Deep Convolutional Networks 11. Creating and Visualizing Word Vectors Using Word2Vec 12. Creating a Movie Recommendation Engine with Keras 13. Image Classification with TensorFlow on Spark 14. Other Books You May Enjoy

Who this book is for

This book is for anyone with a basic understanding of machine learning and big data concepts and who is looking to expand their understanding through a top-down rather than a bottom-up approach. This book gives access to deep learning as well as machine learning algorithms in a plug-and-play fashion. Anyone without previous programming experience, especially with Python, can easily implement the algorithms in this book by following the recipes step by step as instructed. Most of the code in this book is self-explanatory. Each code block performs one particular function or executes on the action in mining, manipulating, transforming, and fitting data to deep learning models.

This book is intended to give the reader both hands-on experience through fun projects such as stock price prediction, as well as a more solid understanding of deep learning and machine learning concepts This is possible the numerous links provided to online resources, such as published papers, tutorials, and guides, throughout every chapter in the book.

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