Hands-On Deep Learning with Apache Spark电子书

Title Page

Copyright and Credits

Hands-On Deep Learning with Apache Spark

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Preface

Who this book is for

What this book covers

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Conventions used

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The Apache Spark Ecosystem

Apache Spark fundamentals

Getting Spark

RDD programming

Spark SQL, Datasets, and DataFrames

Spark Streaming

Cluster mode using different managers

Standalone mode

Mesos cluster mode

YARN cluster mode

Submitting Spark applications on YARN

Kubernetes cluster mode

Summary

Deep Learning Basics

Introducing DL

DNNs overview

CNNs

RNNs

Practical applications of DL

Summary

Extract, Transform, Load

Training data ingestion through Spark

The DeepLearning4j framework

Data ingestion through DataVec and transformation through Spark

Training data ingestion from a database with Spark

Data ingestion from a relational database

Data ingestion from a NoSQL database

Data ingestion from S3

Raw data transformation with Spark

Summary

Streaming

Streaming data with Apache Spark

Streaming data with Kafka and Spark

Apache Kakfa

Spark Streaming and Kafka

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

Backpropagation Through Time (BPTT)

RNN issues

Use cases

Hands-on RNNs with Spark

RNNs with DL4J

RNNs with DL4J and Spark

Loading multiple CSVs for RNN data pipelines

Summary

Training Neural Networks with Spark

Distributed network training with Spark and DeepLearning4j

CNN distributed training with Spark and DL4J

RNN distributed training with Spark and DL4J

Performance considerations

Hyperparameter optimization

The Arbiter UI

Summary

Monitoring and Debugging Neural Network Training

Monitoring and debugging neural networks during their training phases

8.1.1 The DL4J training UI

8.1.2 The DL4J training UI and Spark

8.1.3 Using visualization to tune a network

Summary

Interpreting Neural Network Output

Evaluation techniques with DL4J

Evaluation for classification

Evaluation for classification – Spark example

Other types of evaluation

Summary

Deploying on a Distributed System

Setup of a distributed environment with DeepLearning4j

Memory management

CPU and GPU setup

Building a job to be submitted to Spark for training

Spark distributed training architecture details

Model parallelism and data parallelism

Parameter averaging

Asynchronous stochastic gradient sharing

Importing Python models into the JVM with DL4J

Alternatives to DL4J for the Scala programming language

BigDL

DeepLearning.scala

Summary

NLP Basics

NLP

Tokenizers

Sentence segmentation

POS tagging

Named entity extraction (NER)

Chunking

Parsing

Hands-on NLP with Spark

Hands-on NLP with Spark and Stanford core NLP

Hands-on NLP with Spark NLP

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

Convolution

Object recognition strategies

Convolution applied to image recognition

Keras implementation

DL4J implementation

Summary

Image Classification

Implementing an end-to-end image classification web application

Picking up a proper Keras model

Importing and testing the model in DL4J

Re-training the model in Apache Spark

Implementing the web application

Implementing a web service

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

Appendix A: Functional Programming in Scala

Functional programming (FP)

Purity

Recursion

Appendix B: Image Data Preparation for Spark

Image preprocessing

Strategies

Training

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