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Apache Spark Deep Learning Cookbook
By :
Apache Spark Deep Learning Cookbook
By:
Overview of this book
Organizations these days need to integrate popular big data tools such as Apache Spark with highly efficient deep learning libraries if they’re looking to gain faster and more powerful insights from their data. With this book, you’ll discover over 80 recipes to help you train fast, enterprise-grade, deep learning models on Apache Spark.
Each recipe addresses a specific problem, and offers a proven, best-practice solution to difficulties encountered while implementing various deep learning algorithms in a distributed environment. The book follows a systematic approach, featuring a balance of theory and tips with best practice solutions to assist you with training different types of neural networks such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs). You’ll also have access to code written in TensorFlow and Keras that you can run on Spark to solve a variety of deep learning problems in computer vision and natural language processing (NLP), or tweak to tackle other problems encountered in deep learning.
By the end of this book, you'll have the skills you need to train and deploy state-of-the-art deep learning models on Apache Spark.
Table of Contents (15 chapters)
Preface
Free Chapter
Setting Up Spark for Deep Learning Development
Creating a Neural Network in Spark
Pain Points of Convolutional Neural Networks
Pain Points of Recurrent Neural Networks
Predicting Fire Department Calls with Spark ML
Using LSTMs in Generative Networks
Natural Language Processing with TF-IDF
Real Estate Value Prediction Using XGBoost
Predicting Apple Stock Market Cost with LSTM
Face Recognition Using Deep Convolutional Networks
Creating and Visualizing Word Vectors Using Word2Vec
Creating a Movie Recommendation Engine with Keras
Image Classification with TensorFlow on Spark
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