It is common practice (as you will see next through the code examples of this chapter and from Chapter 7, Training Neural Networks with Spark, onward) to periodically insert a pooling layer between successive convolution layers in a CNN model. This kind of layers scope is to progressively reduce the number of parameters for the network (which translates into a significant lowering of the computation costs). In fact, spatial pooling (which is also found in literature as downsampling or subsampling) is a technique that reduces the dimensionality of each feature map, while at the same time retaining the most important part of the information. Different types of spatial pooling exist. The most used are max, average, sum, and L2-norm.
Let's take as an example, max pooling. This technique requires defining a spatial neighborhood (typically a 2 × 2 window);...
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.
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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