Develop fast, efficient distributed deep learning models with Apache Spark.
Deep Learning is a subset of Machine Learning whereby datasets with several layers of complexity can be processed efficiently. This tutorial brings together two of the most popular buzzwords of today-big data and Artificial Intelligence-by showing you how you can implement Deep Learning solutions using the power of Apache Spark.
The tutorial begins by explaining the fundamentals of Apache Spark and deep learning. You will set up a Spark environment to perform deep learning and learn about the different types of neural net and the principles of distributed modeling (model- and data-parallelism, and more). You will then implement deep learning models (such as CNN, RNN, LTSMs) on Spark, acquire hands-on experience of what it takes, and get a general feeling for the complexity we are dealing with. You will also see how you can use libraries such as Deeplearning4j to perform deep learning on a distributed CPU and GPU setup.
By the end of this course, you’ll have gained experience by implementing models for applications such as object recognition, text analysis, and voice recognition. You will even have designed human expert games.
The code bundle for this course is available at https://github.com/PacktPublishing/Deep-Learning-with-Apache-Spark
About the Author
Tomasz Lelek is a Software Engineer who programs mostly in Java and Scala. He has worked with Spark API and the ML API for the past five years and has production experience in processing petabytes of data.
He is passionate about nearly everything associated with software development and believes that we should always try to consider different solutions and approaches before solving a problem. He was a speaker at conferences in Poland, Confitura and JDD (Java Developers Day), and the Krakow Scala User Group. He has also conducted a live coding session at Geecon Conference.