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Python Deep Learning

You're reading from Python Deep Learning

Product type Book

Published in Apr 2017

Publisher Packt

ISBN-13 9781786464453

Pages 406 pages

Edition 1st Edition

Languages

Concepts

Authors (4):

Valentino Zocca

Gianmario Spacagna

Daniel Slater

Peter Roelants

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Table of Contents (18) Chapters

Python Deep Learning

Credits

About the Authors

About the Reviewer

www.PacktPub.com

Customer Feedback

Preface

1. Machine Learning – An Introduction

2. Neural Networks

3. Deep Learning Fundamentals

4. Unsupervised Feature Learning

5. Image Recognition

6. Recurrent Neural Networks and Language Models

7. Deep Learning for Board Games

8. Deep Learning for Computer Games

9. Anomaly Detection

10. Building a Production-Ready Intrusion Detection System

Index

GPU versus CPU

One of the reasons for the popularity of deep learning today is the drastically increased processing capacity of GPUs (Graphical Processing Units). Architecturally, the CPU (Central Processing Unit) is composed of a few cores that can handle a few threads at a time, while GPUs are composed of hundreds of cores that can handle thousands of threads at the same time. A GPU is a highly parallelizable unit, compared to the CPU that is mainly a serial unit.

DNNs are composed of several layers, and each layer has neurons that behave in the same manner. Moreover, we have discussed how the activity value for each neuron is , or, if we express it in matrix form, we have a = wx, where a and x are vectors and w a matrix. All activation values are calculated in the same way across the network. CPUs and GPUs have a different architecture, in particular they are optimized differently: CPUs are latency optimized and GPUs are bandwidth optimized. In a deep neural network with many layers and...

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Authors (4)

Valentino Zocca

Valentino Zocca has a PhD degree and graduated with a Laurea in mathematics from the University of Maryland, USA, and University of Rome, respectively, and spent a semester at the University of Warwick. He started working on high-tech projects of an advanced stereo 3D Earth visualization software with head tracking at Autometric, a company later bought by Boeing. There he developed many mathematical algorithms and predictive models, and using Hadoop he automated several satellite-imagery visualization programs. He has worked as an independent consultant at the U.S. Census Bureau, in the USA and in Italy. Currently, Valentino lives in New York and works as an independent consultant to a large financial company.

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Gianmario Spacagna

Gianmario Spacagna is a senior data scientist at Pirelli, processing sensors and telemetry data for internet of things (IoT) and connected-vehicle applications. He works closely with tire mechanics, engineers, and business units to analyze and formulate hybrid, physics-driven, and data-driven automotive models. His main expertise is in building ML systems and end-to-end solutions for data products. He holds a master's degree in telematics from the Polytechnic of Turin, as well as one in software engineering of distributed systems from KTH, Stockholm. Prior to Pirelli, he worked in retail and business banking (Barclays), cyber security (Cisco), predictive marketing (AgilOne), and did some occasional freelancing.

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Daniel Slater

Daniel Slater started programming at age 11, developing mods for the id Software game Quake. His obsession led him to become a developer working in the gaming industry on the hit computer game series Championship Manager. He then moved into finance, working on risk- and high-performance messaging systems. He now is a staff engineer working on big data at Skimlinks to understand online user behavior. He spends his spare time training AI to beat computer games. He talks at tech conferences about deep learning and reinforcement learning; and the name of his blog is Daniel Slater's blog. His work in this field has been cited by Google.

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Peter Roelants

Peter Roelants holds a master's in computer science with a specialization in AI from KU Leuven. He works on applying deep learning to a variety of problems, such as spectral imaging, speech recognition, text understanding, and document information extraction. He currently works at Onfido as a team leader for the data extraction research team, focusing on data extraction from official documents.

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