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.

What You Will Learn

Get to know basic Apache Spark and deep learning concepts
Explore deep learning neural networks such as RBM, RNN, and DBN using some of the most popular industrial deep learning frameworks
Learn how to leverage big data to solve real-world problems using deep learning
Understand how to formulate real-world prediction problems as machine learning tasks, how to choose the right neural net architecture for a problem, and how to train neural nets using DL4J
Get up-and-running and gain an insight into the deep learning library DL4J and its practical uses
Design successful solutions with Extreme Learning machines
Train and test neural networks to fit your data model


https://www.packtpub.com/big-data-and-business-intelligence/deep-learning-apache-spark-video