Book Image

Apache Spark Deep Learning Cookbook

By : Ahmed Sherif, Amrith Ravindra
Book Image

Apache Spark Deep Learning Cookbook

By: Ahmed Sherif, Amrith Ravindra

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 (21 chapters)
Title Page
Copyright and Credits
Packt Upsell
Foreword
Contributors
Preface
Index

Contributors

About the authors

Ahmed 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.

I would like to begin by thanking my wife, Ameena, and my three lovely children, Safiya, Hamza, and Layla, for giving me the strength and support to complete this book. I could not have done it without their love and support. I would also like to thank my co-author, Amrith, for all of his hard work and determination to write this book with me.

 

Amrith 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 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.

I would like to begin by thanking Ahmed for giving me this opportunity to work alongside him. Working on this book has been a better learning experience for me than college itself. Next, I would like to thank my mum dad and sister, who have continued to give me motivation and instilled in me the desire to succeed. Finally, I would like to thank my friends, without whose criticism I would have never grown so much as a human. 

About the reviewers

Michal Malohlava, the creator of Sparkling Water, is a geek and developer; as well as being a Java, Linux, programming languages enthusiast who has been developing software for over 10 years. He obtained his Ph.D. from Charles University in Prague in 2012 and post-doctorate from Purdue University. He participates in the development of the H2O platform for advanced big data math and computation, and its incorporation in into Spark engine published as a project called Sparkling Water.

 

 

Adnan Masood,PhD., is an AI and ML researcher, software architect, and Microsoft MVP for Data Platform. He currently works at UST Global as Chief Architect of AI and ML, where he collaborates with Stanford Artificial Intelligence Lab, and MIT AI Lab for building enterprise solutions. A Visiting Scholar at Stanford University and author of Amazon bestseller in programming languages, Functional Programming with F#, His recent talk at Women in Technology Conference, Denver highlighted the importance of diversity in STEM and technology areas and was featured by a variety of news outlets.

 

 

 

 

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