Ahmed Sherif, Amrith Ravindra

Apache Spark Deep Learning Cookbook (eBook, ePUB)

Over 80 recipes that streamline deep learning in a distributed environment with Apache Spark

• Format: ePub

Produktbeschreibung

A solution-based guide to put your deep learning models into production with the power of Apache Spark

Key Features

• 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
  

Book Description

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.

What you will learn

• 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

Who this book is for

If you're looking for a practical and highly useful resource for implementing efficiently distributed deep learning models with Apache Spark, then the Apache Spark Deep Learning Cookbook is for you. Knowledge of the core machine learning concepts and a basic understanding of the Apache Spark framework is required to get the best out of this book. Additionally, some programming knowledge in Python is a plus.

Produktdetails

• Verlag: Packt Publishing
• Seitenzahl: 474
• Erscheinungstermin: 13. Juli 2018
• Englisch
• ISBN-13: 9781788471558
• Artikelnr.: 54533145

Autorenporträt

Ahmed SherifAhmed Sherif is a data scientist who has been working 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 ML and predictive modeling using both Python and R. Lately, he has been developing ML 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.Amrith RavindraAmrith Ravindra is a machine learning enthusiast who holds degrees in electrical and industrial engineering. While pursuing his masters he dove deeper into the world of ML and developed the love for data science. Graduate level courses in engineering gave him the mathematical background to launch himself into a career in ML. 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 ML algorithms. He hopes that this book will help him achieve his ultimate goal of becoming a data scientist and actively contributing to ML.