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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

GoogleNet Inception V3 model

As a concrete implementation of a CNN, in this section, I am going to present the GoogleNet (https://ai.google/research/pubs/pub43022) architecture by Google (https://www.google.com/) and its inception layers. It has been presented at the ImageNet Large Scale Visual Recognition Challenge 2014 (ILSVRC2014, http://www.image-net.org/challenges/LSVRC/2014/). Needless to say, it won that competition. The distinct characteristic of this implementation is the following: increased depth and width and, at the same time, a constant computational budget. Improved computing resources utilization is part of the network design.

This chart summarizes all of the layers for this network implementation presented in the context:

Figure 5.4: GoogleNet layers

There are 22 layers with parameters (excluding the pooling layers; the total is 27 if they are included) and almost...

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