Book Image

Hands-On Deep Learning with Apache Spark

By : Guglielmo Iozzia
Book Image

Hands-On Deep Learning with Apache Spark

By: Guglielmo 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.

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Table of Contents (19 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
Free Chapter
1
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
2
Deep Learning Basics
Deep Learning Basics
Introducing DL
DNNs overview
Practical applications of DL
Summary
3
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
4
Streaming
Streaming
Streaming data with Apache Spark
Streaming data with Kafka and Spark
Streaming data with DL4J and Spark
Summary
5
Convolutional Neural Networks
Convolutional Neural Networks
Convolutional layers
Pooling layers
Fully connected layers
Weights
GoogleNet Inception V3 model
Hands-on CNN with Spark
Summary
6
Recurrent Neural Networks
Recurrent Neural Networks
LSTM
Use cases
Hands-on RNNs with Spark
Summary
7
Training Neural Networks with Spark
Training Neural Networks with Spark
Distributed network training with Spark and DeepLearning4j
Hyperparameter optimization
Summary
8
Monitoring and Debugging Neural Network Training
Monitoring and Debugging Neural Network Training
Monitoring and debugging neural networks during their training phases
Summary
9
Interpreting Neural Network Output
Interpreting Neural Network Output
Evaluation techniques with DL4J
Other types of evaluation
Summary
10
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
11
NLP Basics
NLP Basics
NLP
Hands-on NLP with Spark
Summary
12
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
13
Convolution
Convolution
Convolution
Object recognition strategies
Convolution applied to image recognition
Summary
14
Image Classification
Image Classification
Implementing an end-to-end image classification web application
Summary
15
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
16
Other Books You May Enjoy
Other Books You May Enjoy
Leave a review - let other readers know what you think
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

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...

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