Book Image

Hands-On Deep Learning with Apache Spark

By : Guglielmo Iozzia
Book Image

Hands-On Deep Learning with Apache Spark

By: Guglielmo Iozzia

Overview of this book

Deep learning is a subset of machine learning where datasets with several layers of complexity can be processed. Hands-On Deep Learning with Apache Spark addresses the sheer complexity of technical and analytical parts and the speed at which deep learning solutions can be implemented on Apache Spark. The book starts with the fundamentals of Apache Spark and deep learning. You will set up Spark for deep learning, learn principles of distributed modeling, and understand different types of neural nets. You will then implement deep learning models, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory (LSTM) on Spark. As you progress through the book, you will gain hands-on experience of what it takes to understand the complex datasets you are dealing with. During the course of this book, you will use popular deep learning frameworks, such as TensorFlow, Deeplearning4j, and Keras to train your distributed models. By the end of this book, you'll have gained experience with the implementation of your models on a variety of use cases.
Table of Contents (19 chapters)
Appendix A: Functional Programming in Scala
Appendix B: Image Data Preparation for Spark

Summary

After a recap of the concept of convolution and the classification of object recognition strategies, in this chapter, we have been implementing and training CNNs for object recognition using different languages (Python and Scala) and different open source frameworks (Keras and TensorFlow in the first case, DL4J, ND4J, and Apache Spark in the second) in a hands-on manner.

In the next chapter, we are going to implement a full image classification web application which, behind the scenes, uses a combination of Keras, TensorFlow, DL4J, ND4J, and Spark.