Distributed Deep Learning with Apache Spark

Deep learning is a subfield of Artificial Intelligence and Machine Learning where a huge amount of data is processed in complex layers of neural networks. It has solved tons of interesting real-world problems in recent years. Distributed deep learning (DL) involves training a deep neural network in parallel across multiple machines. In this course, you will get started with implementing Deep Learning solutions easily with the help of Apache Spark.

You will begin with a short introduction on Deep Learning and Apache Spark and the principles of distributed modeling. With the help of real-world examples, you will investigate different types of neural network and work with DL libraries such as BigDL, Deeplearning4j, and the Deep Learning pipelines library to implement DL models and distributed computing on Spark. You will see how you can easily use a large dataset to implement efficient DL solutions to simplify real-world examples. You will also learn how to distribute the computationally heavy parts of DL into processes with the help of Apache Spark.

By the end of this course, you'll have gained experience in implementing Distributed Deep Learning for your models at work. Our examples will be based on real-world problems from the banking industry.

About the Author

Tomasz Lelek is a Software Engineer, programming mostly in Java, Scala. He has been working with the Spark and ML APIs for the past 5 years with 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. Recently he was a speaker at conferences in Poland: Confitura and JDD (Java Developers Day), and also at Krakow Scala User Group. He has also conducted a live coding session at the Geecon Conference.

He is a co-founder of initlearn, an e-learning platform that was built with the Java language.