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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

Fully connected layers

A fully connected layer is the last layer of a CNN. Fully connected layers, given an input volume, return as output a multi-dimensional vector. The dimension of the output vector matches the number of classes for the particular problem to solve.

This chapter and others in this book present some examples of CNN implementation and training for digit classification purposes. In those cases, the dimension of the output vector would be 10 (the possible digits are 0 to 9). Each number in the 10-dimensional output vector represents the probability of a certain class (digit). The following is an output vector for a digit classification inference:

[0 0 0 .1 .75 .1 .05 0 0 0]

How do we interpret those values? The network is telling us that it believes that the input image is a four with a 75% probability (which is the highest in this case), with a 10% probability...

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