Gianmario Spacagna - Python Deep Learning: Next generation techniques to revolutionize computer vision, AI, speech and data analysis
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- Book:Python Deep Learning: Next generation techniques to revolutionize computer vision, AI, speech and data analysis
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Take your machine learning skills to the next level by mastering Deep Learning concepts and algorithms using Python.
Key Features- Explore and create intelligent systems using cutting-edge deep learning techniques
- Implement deep learning algorithms and work with revolutionary libraries in Python
- Get real-world examples and easy-to-follow tutorials on Theano, TensorFlow, H2O and more
With an increasing interest in AI around the world, deep learning has attracted a great deal of public attention. Every day, deep learning algorithms are used broadly across different industries.
The book will give you all the practical information available on the subject, including the best practices, using real-world use cases. You will learn to recognize and extract information to increase predictive accuracy and optimize results.
Starting with a quick recap of important machine learning concepts, the book will delve straight into deep learning principles using Sci-kit learn. Moving ahead, you will learn to use the latest open source libraries such as Theano, Keras, Googles TensorFlow, and H20. Use this guide to uncover the difficulties of pattern recognition, scaling data with greater accuracy and discussing deep learning algorithms and techniques.
Whether you want to dive deeper into Deep Learning, or want to investigate how to get more out of this powerful technology, youll find everything inside.
What You Will Learn- Get a practical deep dive into deep learning algorithms
- Explore deep learning further with Theano, Caffe, Keras, and TensorFlow
- Learn about two of the most powerful techniques at the core of many practical deep learning implementations: Auto-Encoders and Restricted Boltzmann Machines
- Dive into Deep Belief Nets and Deep Neural Networks
- Discover more deep learning algorithms with Dropout and Convolutional Neural Networks
- Get to know device strategies so you can use deep learning algorithms and libraries in the real world
This book is for Data Science practitioners as well as aspirants who have a basic foundational understanding of Machine Learning concepts and some programming experience with Python. A mathematical background with a conceptual understanding of calculus and statistics is also desired.
Table of Contents- Machine Learning An Introduction
- Neural Networks
- Deep Learning Fundamentals
- Unsupervised Feature Learning
- Image Recognition
- Recurrent Neural Networks and Language Models
- Deep Learning for Board Games
- Deep Learning for Computer Games
- Anomaly Detection
- Building a Production-Ready Intrusion Detection System
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Copyright 2017 Packt Publishing
All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.
Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the authors, nor Packt Publishing, and its dealers and distributors will be held liable for any damages caused or alleged to be caused directly or indirectly by this book.
Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.
First published: April 2017
Production reference: 1270417
Published by Packt Publishing Ltd.
Livery Place
35 Livery Street
Birmingham B3 2PB, UK.
ISBN 978-1-78646-445-3
www.packtpub.com
Authors
Valentino Zocca
Gianmario Spacagna
Daniel Slater
Peter Roelants
Reviewer
Max Pumperla
Commissioning Editor
Akram Hussain
Acquisition Editor
Vinay Argekar
Content Development Editor
Mayur Pawanikar
Technical Editor
Vivek Arora
Copy Editor
Safis Editing
Project Coordinator
Nidhi Joshi
Proofreader
Safis Editing
Indexer
Francy Puthiry
Graphics
Tania Dutta
Production Coordinator
Arvindkumar Gupta
Valentino Zocca graduated with a PhD in mathematics from the University of Maryland, USA, with a dissertation in symplectic geometry, after having graduated with a laurea in mathematics from the University of Rome. He spent a semester at the University of Warwick. After a post-doc in Paris, Valentino started working on high-tech projects in the Washington, D.C. area and played a central role in the design, development, and realization of an advanced stereo 3D Earth visualization software with head tracking at Autometric, a company later bought by Boeing. At Boeing, he developed many mathematical algorithms and predictive models, and using Hadoop, he has also automated several satellite-imagery visualization programs. He has since become an expert on machine learning and deep learning and has worked at the U.S. Census Bureau and as an independent consultant both in the US and in Italy. He has also held seminars on the subject of machine and deep learning in Milan and New York.
Currently, Valentino lives in New York and works as an independent consultant to a large financial company, where he develops econometric models and uses machine learning and deep learning to create predictive models. But he often travels back to Rome and Milan to visit his family and friends.
Gianmario Spacagna is a senior data scientist at Pirelli, processing sensors and telemetry data for IoT and connected-vehicle applications.
He works closely with tyre mechanics, engineers, and business units to analyze and formulate hybrid, physics-driven, and data-driven automotive models.
His main expertise is in building machine learning systems and end-to-end solutions for data products.
He is the coauthor of the Professional Data Science Manifesto (datasciencemanifesto.org) and founder of the Data Science Milan meetup community (datasciencemilan.org).
Gianmario loves evangelizing his passion for best practices and effective methodologies in the community.
He holds a master's degree in telematics from the Polytechnic of Turin and 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 some occasional freelancing.
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; his blog can be found at www.danielslater.net. His work in this field has been cited by Google.
I would like to thank my wife, Judit Kollo, for her love, support, and diagrams. Also thanks to my son, David; mother, Catherine; and father, Don.
Peter Roelants holds a master's in computer science with a specialization in artificial intelligence 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 lead for the data extraction research team, focusing on data extraction from official documents.
Max Pumperla is a data scientist and engineer specializing in deep learning and its applications. He currently holds the position of Head of Data Science at collect Artificial Intelligence GmbH and has previous experience in banking, online marketing, and the SMB market. Being an author and maintainer of several Python packages, his open source footprint includes contributions to popular machine learning libraries such as Keras and Hyperopt. He holds a PhD in algebraic geometry from the University of Hamburg.
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