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Hands-On Deep Learning with Apache Spark

By : Iozzia

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.

Preface

Preface

Who this book is for

What this book covers

To get the most out of this book

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Free Chapter

The Apache Spark Ecosystem

The Apache Spark Ecosystem

Apache Spark fundamentals

Getting Spark

RDD programming

Spark SQL, Datasets, and DataFrames

Spark Streaming

Cluster mode using different managers

Summary

Deep Learning Basics

Deep Learning Basics

Introducing DL

DNNs overview

Practical applications of DL

Summary

Extract, Transform, Load

Extract, Transform, Load

Training data ingestion through Spark

Training data ingestion from a database with Spark

Data ingestion from S3

Raw data transformation with Spark

Summary

Streaming

Streaming

Streaming data with Apache Spark

Streaming data with Kafka and Spark

Streaming data with DL4J and Spark

Summary

Convolutional Neural Networks

Convolutional Neural Networks

Convolutional layers

Pooling layers

Fully connected layers

Weights

GoogleNet Inception V3 model

Hands-on CNN with Spark

Summary

Recurrent Neural Networks

Recurrent Neural Networks

LSTM

Use cases

Hands-on RNNs with Spark

Summary

Training Neural Networks with Spark

Training Neural Networks with Spark

Distributed network training with Spark and DeepLearning4j

Hyperparameter optimization

Summary

Monitoring and Debugging Neural Network Training

Monitoring and Debugging Neural Network Training

Monitoring and debugging neural networks during their training phases

Summary

Interpreting Neural Network Output

Interpreting Neural Network Output

Evaluation techniques with DL4J

Other types of evaluation

Summary

Deploying on a Distributed System

Deploying on a Distributed System

Setup of a distributed environment with DeepLearning4j

Spark distributed training architecture details

Importing Python models into the JVM with DL4J

Alternatives to DL4J for the Scala programming language

Summary

NLP Basics

NLP Basics

NLP

Hands-on NLP with Spark

Summary

Textual Analysis and Deep Learning

Textual Analysis and Deep Learning

Hands-on NLP with DL4J

Hands-on NLP with TensorFlow

Hand-on NLP with Keras and a TensorFlow backend

Hands-on NLP with Keras model import into DL4J

Summary

Convolution

Convolution

Convolution

Object recognition strategies

Convolution applied to image recognition

Summary

Image Classification

Image Classification

Implementing an end-to-end image classification web application

Summary

What's Next for Deep Learning?

What's Next for Deep Learning?

What to expect next for deep learning and AI

Topics to watch for

Is Spark ready for RL?

DeepLearning4J future support for GANs

Summary

Other Books You May Enjoy

Other Books You May Enjoy

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Appendix A: Functional Programming in Scala

Appendix A: Functional Programming in Scala

Functional programming (FP)

Appendix B: Image Data Preparation for Spark

Appendix B: Image Data Preparation for Spark

Image preprocessing

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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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