Hands-On Deep Learning with Apache Spark

By : Iozzia

Buy this Book

Hands-On Deep Learning with Apache Spark

By: Iozzia

Buy this Book

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

Who this book is for

What this book covers

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Free Chapter

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

Introducing DL

DNNs overview

Practical applications of DL

Summary

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 data with Apache Spark

Streaming data with Kafka and Spark

Streaming data with DL4J and Spark

Summary

Convolutional Neural Networks

Convolutional layers

Pooling layers

Fully connected layers

Weights

GoogleNet Inception V3 model

Hands-on CNN with Spark

Summary

Recurrent Neural Networks

LSTM

Use cases

Hands-on RNNs with Spark

Summary

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 networks during their training phases

Summary

Interpreting Neural Network Output

Evaluation techniques with DL4J

Other types of evaluation

Summary

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

Hands-on NLP with Spark

Summary

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

Object recognition strategies

Convolution applied to image recognition

Summary

Image Classification

Implementing an end-to-end image classification web application

Summary

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

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Appendix A: Functional Programming in Scala

Functional programming (FP)

Appendix B: Image Data Preparation for Spark

Image preprocessing

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Other types of evaluation

Other evaluations are available through the DL4J API. This section lists them.

It is possible to evaluate a network performing regression through the RegressionEvaluation class (https://static.javadoc.io/org.deeplearning4j/deeplearning4j-nn/1.0.0-alpha/org/deeplearning4j/eval/RegressionEvaluation.html, DL4J NN). With reference to the example that we used in the Evaluation for classification section, evaluation for regression can be done the following way:

val eval = new RegressionEvaluation(3)
 val output = model.output(testData.getFeatureMatrix)
 eval.eval(testData.getLabels, output)
 println(eval.stats)

The produced output of the stats method includes the MSE, the MAE, the RMSE, the RSE, and the R^2:

ROC (short for Receiver Operating Characteristic, https://en.wikipedia.org/wiki/Receiver_operating_characteristic) is another commonly used metric for the...