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Learning PySpark

You're reading from  Learning PySpark

Product type Book
Published in Feb 2017
Publisher Packt
ISBN-13 9781786463708
Pages 274 pages
Edition 1st Edition
Languages
Authors (2):
Tomasz Drabas Tomasz Drabas
Profile icon Tomasz Drabas
Denny Lee Denny Lee
Profile icon Denny Lee
View More author details
Toc

Table of Contents (20) Chapters close

Learning PySpark
Credits
Foreword
About the Authors
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
1. Understanding Spark 2. Resilient Distributed Datasets 3. DataFrames 4. Prepare Data for Modeling 5. Introducing MLlib 6. Introducing the ML Package 7. GraphFrames 8. TensorFrames 9. Polyglot Persistence with Blaze 10. Structured Streaming 11. Packaging Spark Applications Index

What is Deep Learning?


Deep Learning is part of a family of machine learning techniques based on learning representations of data. Deep Learning is loosely based on our brain's own neural networks, the purpose of this structure is to provide a large number of highly interconnected elements (in biological systems, this would be the neurons in our brains); there are approximately 100 billion neurons in our brain, each connected to approximately 10,000 other neurons, resulting in a mind-boggling 1015 synaptic connections. These elements work together to solve problems through learning processes – examples include pattern recognition and data classification.

Learning within this architecture involves modifications of the connections between the interconnected elements similar to how our own brains make adjustments to the synaptic connections between neurons:

Source: Wikimedia Commons: File: Réseau de neurones.jpg; https://commons.wikimedia.org/wiki/File:Réseau_de_neurones.jpg.

The traditional algorithmic...

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