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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

Hands-on RNNs with Spark

Let's start now being hands-on with RNNs. This section is divided into two parts—the first one is about using DL4J to implement a network, while the second one will introduce using both DL4J and Spark for the same purpose. As with CNNs, you will discover that, thanks to the DL4J framework, lots of high-level facilities come out-of-the-box with it, so that the implementation process is easier than you might expect.

RNNs with DL4J

The first example presented in this chapter is an LSTM which, after the training, will recite the following characters once the first character of the learning string has been used as input for it.

The dependencies for this example are the following:

  • Scala 2.11...

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