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

Spark distributed training architecture details

The Distributed network training with Spark and DeepLearning4J section in Chapter 7, Training Neural Networks with Spark, explains why it is important to train MNNs in a distributed way across a cluster, and states that DL4J uses a parameter averaging approach to parallel training. This section goes through the architecture details of the distributed training approaches (parameter averaging and gradient sharing, which replaced the parameter averaging approach in DL4J starting from release 1.0.0-beta of the framework). The way DL4J approaches distributed training is transparent to developers, but it is good to have knowledge of it anyway.

Model parallelism and data parallelism

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