Deep Learning is a partially subset of Machine Learning wherein 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 with explaining the fundamentals of Apache Spark and deep learning. You will see how to set up the Spark environment for performing deep learning, and learn different types of neural nets and principles of distributed modelling (model- and data-parallelism etc.). You will then implement deep learning models like CNN, RNN, LTSMs on Spark and get hands-on experience of what it takes and a general feeling of the complexity we are dealing with. You will also see how you can use libraries such as Deeplearning4j for performing deep learning on a distributed CPU and GPU setup.
By the end of this course, you'll have gained experience with implementations of your models for applications such as object recognition, text analysis, voice recognition and even design human expert games.
WHAT WILL YOU LEARN
- Get to know the basic Apache Spark and deep learning concepts
- Explore deep learning neural networks such as RBM, RNN, DBN using some of the most popular industrail 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 neutral net architecture for the problem and how to train neutral nets using DL4J
- Get up and running to 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