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Hands-On Deep Learning with Apache Spark

Hands-On Deep Learning with Apache Spark

By : Iozzia
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Hands-On Deep Learning with Apache Spark

Hands-On Deep Learning with Apache Spark

By: 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)
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Appendix A: Functional Programming in Scala
Appendix B: Image Data Preparation for Spark

Textual Analysis and Deep Learning

In the previous chapter, we became familiar with the core concepts of Natural Language Processing (NLP) and then we saw some implementation examples in Scala with Apache Spark, and two open source libraries for this framework. We also understood the pros and cons of those solutions. This chapter walks through hands-on examples of NLP use case implementations using DL (Scala and Spark). The following four cases will be covered:

  • DL4J
  • TensorFlow
  • Keras and TensorFlow backend
  • DL4J and Keras model import

The chapter covers some considerations regarding the pros and cons for each of those DL approaches in order, so that readers should then be ready to understand in which cases one framework is preferred over the others.

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