Before any training can begin, ML techniques in general, and so DL techniques, have a set of parameters that have to be chosen. They are referred to as hyperparameters. Keeping focus on DL, we can say that some of these (the number of layers and their size) define the architecture of a neural network, while others define the learning process (learning rate, regularization, and so on). Hyperparameter optimization is an attempt to automate this process (that has a significant impact on the results achieved by training a neural network) using a dedicated software that applies some search strategies. DL4J provides a tool, Arbiter, for hyperparameter optimization of neural nets. This tool doesn't fully automate the process—a manual intervention from data scientists or developers is needed in order to specify the search spaces (the ranges of valid...
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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