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.
If you are a data scientist and want to learn how to use Spark to implement efficient deep learning models, this is the course for you. Knowledge of some machine learning concepts and some exposure to Spark are assumed.
Use deep learning method with Apache Spark to stay on the cutting edge of ML techniques
Understand DL neural networks with versatile code and examples from the real world
Learn about deep learning algorithms running on the DL4J framework and how they compare with other popular DL frameworks