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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 wraps up this book. In this book, we got familiar with Apache Spark and its components, and then we moved on to discover the fundamentals of DL before getting practical. We started our Scala hands-on journey with the DL4J framework by understanding how to ingest training and testing data from diverse data sources (in both batch and streaming modes) and transform it into vectors through the DataVec library. The journey then moved on to exploring the details of CNNs and RNNs the implementation of those network models through DL4J, how to train them in a distributed and Spark-based environment, how to get useful insights by monitoring them using the visual facilities of DL4J, and how to evaluate their efficiency and do inference.

We also learned some tips and best practices that we should use when configuring a production environment for training, and how it...

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