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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

Convolution

Chapter 5, Convolutional Neural Networks, covered the theory behind CNNs, and convolution of course has been part of that presentation. Let's do a recap of this concept from a mathematical and practical perspective before moving on to object recognition. In mathematics, convolution is an operation on two functions that produces a third function, which is the result of the integral of the product between the first two, one of which is flipped:

Convolution is heavily used in 2D image processing and signal filtering.

To better understand what happens behind the scenes, here's a simple Python code example of 1D convolution with NumPy (http://www.numpy.org/):

import numpy as np

x = np.array([1, 2, 3, 4, 5])
y = np.array([1, -2, 2])
result = np.convolve(x, y)
print result

This produces the following result:

Let's see how the convolution between the x and y arrays...

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