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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

Summary

This chapter closes the explanation of the NLP implementation process with Scala. In this chapter and the previous one, we evaluated different frameworks for this programming language, and the pros and cons of each have been detailed. In this chapter, the focus has been mostly on a DL approach to NLP. For that, some Python alternatives have been presented, and the potential integration of those Python models in a JVM context with the DL4J framework has been highlighted. At this stage, a reader should be able to accurately evaluate what will be the best fit for his/her particular NLP use case.

Starting from the next chapter, we will learn more about convolution and how CNNs apply to image recognition problems. Image recognition will be explained by presenting different implementations using different frameworks, including DL4J, Keras, and TensorFlow.

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