更新时间：2021-07-02 13:34:49
coverpage
Title Page
Copyright and Credits
Hands-On Deep Learning with Apache Spark
About Packt
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Contributors
About the author
About the reviewer
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Preface
Who this book is for
What this book covers
To get the most out of this book
Download the example code files
Download the color images
Conventions used
Get in touch
Reviews
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
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
Streaming
Streaming data with Apache Spark
Streaming data with Kafka and Spark
Apache Kakfa
Spark Streaming and Kafka
Streaming data with DL4J and Spark
Convolutional Neural Networks
Convolutional layers
Pooling layers
Fully connected layers
Weights
GoogleNet Inception V3 model
Hands-on CNN with Spark
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
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
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
Interpreting Neural Network Output
Evaluation techniques with DL4J
Evaluation for classification
Evaluation for classification – Spark example
Other types of evaluation
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
