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Python Deep Learning

You're reading from Python Deep Learning Next generation techniques to revolutionize computer vision, AI, speech and data analysis

Product type Paperback

Published in Apr 2017

Publisher Packt

ISBN-13 9781786464453

Length 406 pages

Edition 1st Edition

Languages

Python

Tools

TensorFlow

Concepts

Computer Vision

Authors (4):

Peter Roelants

Daniel Slater

Valentino Zocca

Gianmario Spacagna

View More author details

Table of Contents (12) Chapters

Preface

1. Machine Learning – An Introduction FREE CHAPTER

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

Testing

Before we discuss what testing means in data science, let's summarize a few concepts.

Firstly and in general, what is a model in science? We can cite the following definitions:

In science, a model is a representation of an idea, an object or even a process or a system that is used to describe and explain phenomena that cannot be experienced directly.
Scientific Modelling, Science Learning Hub, http://sciencelearn.org.nz/Contexts/The-Noisy-Reef/Science-Ideas-and-Concepts/Scientific-modelling

And this:

A scientific model is a conceptual, mathematical or physical representation of a real-world phenomenon. A model is generally constructed for an object or process when it is at least partially understood, but difficult to observe directly. Examples include sticks and balls representing molecules, mathematical models of planetary movements or conceptual principles like the ideal gas law. Because of the infinite variations actually found in nature, all but the simplest and most vague models...

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Authors (4)

Zocca

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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Roelants

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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Spacagna

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

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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