The previous chapter focused on training Multilayer Neural Networks (MNNs), and presenting code examples for CNNs and RNNs in particular. This chapter describes how monitoring a network can be done while training is in progress and how to use this monitoring information to tune a model. DL4J provides UI facilities for monitoring and tuning purposes, and will be the centerpiece of this chapter. These facilities also work in a training context with DL4J and Apache Spark. Examples for both situations (training using DL4J only and DL4J with Spark) will be presented. A list of potential baseline steps or tips for network training will also be discussed.
Hands-On Deep Learning with Apache Spark
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Hands-On Deep Learning with Apache Spark
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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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Current Title:
Hands-On Deep Learning with Apache Spark
Table of Contents (19 chapters)
Preface
Free Chapter
The Apache Spark Ecosystem
Deep Learning Basics
Extract, Transform, Load
Streaming
Convolutional Neural Networks
Recurrent Neural Networks
Training Neural Networks with Spark
Monitoring and Debugging Neural Network Training
Interpreting Neural Network Output
Deploying on a Distributed System
NLP Basics
Textual Analysis and Deep Learning
Convolution
Image Classification
What's Next for Deep Learning?
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Appendix A: Functional Programming in Scala
Appendix B: Image Data Preparation for Spark
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