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Apache Spark Deep Learning Cookbook

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  • Set up a fully functional Spark environment
  • Understand practical machine learning and deep learning concepts
  • Apply built-in machine learning libraries within Spark
  • Explore libraries that are compatible with TensorFlow and Keras
  • Explore NLP models such as word2vec and TF-IDF on Spark
  • Organize dataframes for deep learning evaluation
  • Apply testing and training modeling to ensure accuracy
  • Access readily available code that may be reusable

With deep learning gaining rapid mainstream adoption in modern-day industries, organizations are looking for ways to unite popular big data tools with highly efficient deep learning libraries. As a result, this will help deep learning models train with higher efficiency and speed.

With the help of the Apache Spark Deep Learning Cookbook, you’ll work through specific recipes to generate outcomes for deep learning algorithms, without getting bogged down in theory. From setting up Apache Spark for deep learning to implementing types of neural net, this book tackles both common and not so common problems to perform deep learning on a distributed environment. In addition to this, you’ll get access to deep learning code within Spark that can be reused to answer similar problems or tweaked to answer slightly different problems. You will also learn how to stream and cluster your data with Spark. Once you have got to grips with the basics, you’ll explore how to implement and deploy deep learning models, such as Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) in Spark, using popular libraries such as TensorFlow and Keras.

By the end of the book, you'll have the expertise to train and deploy efficient deep learning models on Apache Spark.

  • Discover practical recipes for distributed deep learning with Apache Spark
  • Learn to use libraries such as Keras and TensorFlow
  • Solve problems in order to train your deep learning models on Apache Spark
Page Count 474
Course Length 14 hours 13 minutes
Date Of Publication 12 Jul 2018
Introduction to feedforward networks
Sequential workings of RNNs
Pain point #1 – The vanishing gradient problem
Pain point #2 – The exploding gradient problem
Sequential working of LSTMs


Amrith Ravindra

Amrith Ravindra is a machine learning enthusiast who holds degrees in electrical and industrial engineering. While pursuing his master’s, he dove deeper into the world of machine learning and developed a love for data science. Graduate-level courses in engineering gave him the mathematical background to launch himself into a career in machine learning. He met Ahmed Sherif at a local data science meetup in Tampa. They decided to put their brains together to write a book on their favorite machine learning algorithms. He hopes this book will help him achieve his ultimate goal of becoming a data scientist and actively contributing to machine learning.

Ahmed Sherif

Ahmed Sherif is a data scientist who has worked with data in various roles since 2005. He started off with BI solutions and transitioned to data science in 2013. In 2016, he obtained a master's in Predictive Analytics from Northwestern University, where he studied the science and application of machine learning and predictive modeling using both Python and R. Lately, he has been developing machine learning and deep learning solutions on the cloud using Azure. In 2016, he published his first book, Practical Business Intelligence. He currently works as a Technology Solution Profession in Data and AI for Microsoft.