Comprendre Le Deep Learning

Titre : Comprendre Le Deep Learning
Auteur : Jean-Claude Heudin
Éditeur :
ISBN-13 : 9791091245449
Libération : 2016-11-02

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Apres des resultats spectaculaires, dont la victoire d'AlphaGo sur le meilleur joueur mondial de Go, le Deep Learning suscite autant d'interets que d'interrogations. Inspire au depart par une metaphore biologique, celle du cerveau, le domaine des reseaux de neurones est devenu l'un des principaux axes de recherche de l'intelligence artificielle. Quel que soit le secteur d'activite, pas un seul en effet ne semble echapper aux applications du Deep Learning. Quels sont les principes des reseaux de neurones ? Comment fonctionnent-ils ? Quand et pourquoi les utiliser ? Sont-ils simples amettre en oeuvre ? Qu'est-ce qu'on entend reellement par Deep Learning ? Jean-Claude Heudin propose avec ce livre de repondre a ces questions. Dans un style direct et richement illustre, les explications sont abordables par le plus grand nombre, avec une mise en pratique au travers d'exemples. Pour comprendre le Deep Learning, nul besoin ici d'un fort niveau en mathematiques. Les principes de calcul sont reduits a des operations simples et les exemples de programmation sont accessibles. Ce livre s'adresse a tous ceux qui souhaitent comprendre concretement les enjeux du Deep Learning."

Deep Learning

Titre : Deep Learning
Auteur : Ian Goodfellow
Éditeur : MIT Press
ISBN-13 : 9780262035613
Libération : 2016-11-18

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An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives.

Learning TensorFlow

Titre : Learning TensorFlow
Auteur : Tom Hope
Éditeur : "O'Reilly Media, Inc."
ISBN-13 : 9781491978481
Libération : 2017-08-09

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Roughly inspired by the human brain, deep neural networks trained with large amounts of data can solve complex tasks with unprecedented accuracy. This practical book provides an end-to-end guide to TensorFlow, the leading open source software library that helps you build and train neural networks for computer vision, natural language processing (NLP), speech recognition, and general predictive analytics. Authors Tom Hope, Yehezkel Resheff, and Itay Lieder provide a hands-on approach to TensorFlow fundamentals for a broad technical audience—from data scientists and engineers to students and researchers. You’ll begin by working through some basic examples in TensorFlow before diving deeper into topics such as neural network architectures, TensorBoard visualization, TensorFlow abstraction libraries, and multithreaded input pipelines. Once you finish this book, you’ll know how to build and deploy production-ready deep learning systems in TensorFlow. Get up and running with TensorFlow, rapidly and painlessly Learn how to use TensorFlow to build deep learning models from the ground up Train popular deep learning models for computer vision and NLP Use extensive abstraction libraries to make development easier and faster Learn how to scale TensorFlow, and use clusters to distribute model training Deploy TensorFlow in a production setting

Make Your Own Neural Network

Titre : Make Your Own Neural Network
Auteur : Tariq Rashid
Éditeur : Createspace Independent Publishing Platform
ISBN-13 : 1530826608
Libération : 2016-03-31

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A step-by-step gentle journey through the mathematics of neural networks, and making your own using the Python computer language. Neural networks are a key element of deep learning and artificial intelligence, which today is capable of some truly impressive feats. Yet too few really understand how neural networks actually work. This guide will take you on a fun and unhurried journey, starting from very simple ideas, and gradually building up an understanding of how neural networks work. You won't need any mathematics beyond secondary school, and an accessible introduction to calculus is also included. The ambition of this guide is to make neural networks as accessible as possible to as many readers as possible - there are enough texts for advanced readers already! You'll learn to code in Python and make your own neural network, teaching it to recognise human handwritten numbers, and performing as well as professionally developed networks. Part 1 is about ideas. We introduce the mathematical ideas underlying the neural networks, gently with lots of illustrations and examples. Part 2 is practical. We introduce the popular and easy to learn Python programming language, and gradually builds up a neural network which can learn to recognise human handwritten numbers, easily getting it to perform as well as networks made by professionals. Part 3 extends these ideas further. We push the performance of our neural network to an industry leading 98% using only simple ideas and code, test the network on your own handwriting, take a privileged peek inside the mysterious mind of a neural network, and even get it all working on a Raspberry Pi. All the code in this has been tested to work on a Raspberry Pi Zero.

Neural Networks

Titre : Neural Networks
Auteur : Simon Haykin
Éditeur : IEEE
ISBN-13 : 0780334949
Libération : 1999-01

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Simon Haykin A été écrit sous une forme ou une autre pendant la plus grande partie de sa vie. Vous pouvez trouver autant d'inspiration de Neural Networks Aussi informatif et amusant. Cliquez sur le bouton TÉLÉCHARGER ou Lire en ligne pour obtenir gratuitement le livre de titre $ gratuitement.

Deep Learning Made Easy with R

Titre : Deep Learning Made Easy with R
Auteur : N. D. Lewis
Éditeur : Createspace Independent Publishing Platform
ISBN-13 : 1519514212
Libération : 2016-01-10

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Master Deep Learning with this fun, practical, hands on guide. With the explosion of big data deep learning is now on the radar. Large companies such as Google, Microsoft, and Facebook have taken notice, and are actively growing in-house deep learning teams. Other large corporations are quickly building out their own teams. If you want to join the ranks of today's top data scientists take advantage of this valuable book. It will help you get started. It reveals how deep learning models work, and takes you under the hood with an easy to follow process showing you how to build them faster than you imagined possible using the powerful, free R predictive analytics package. Bestselling decision scientist Dr. N.D Lewis shows you the shortcut up the steep steps to the very top. It's easier than you think. Through a simple to follow process you will learn how to build the most successful deep learning models used for learning from data. Once you have mastered the process, it will be easy for you to translate your knowledge into your own powerful applications. If you want to accelerate your progress, discover the best in deep learning and act on what you have learned, this book is the place to get started. YOU'LL LEARN HOW TO: Understand Deep Neural Networks Use Autoencoders Unleash the power of Stacked Autoencoders Leverage the Restricted Boltzmann Machine Develop Recurrent Neural Networks Master Deep Belief Networks Everything you need to get started is contained within this book. It is your detailed, practical, tactical hands on guide - the ultimate cheat sheet for deep learning mastery. A book for everyone interested in machine learning, predictive analytic techniques, neural networks and decision science. Start building smarter models today using R! Buy the book today. Your next big breakthrough using deep learning is only a page away!

Artificial Intelligence for Humans Volume 2

Titre : Artificial Intelligence for Humans Volume 2
Auteur : Jeff Heaton
Éditeur : CreateSpace
ISBN-13 : 1499720572
Libération : 2014-05-28

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Nature can be a great source of inspiration for artificial intelligence algorithms because its technology is considerably more advanced than our own. Among its wonders are strong AI, nanotechnology, and advanced robotics. Nature can therefore serve as a guide for real-life problem solving. In this book, you will encounter algorithms influenced by ants, bees, genomes, birds, and cells that provide practical methods for many types of AI situations. Although nature is the muse behind the methods, we are not duplicating its exact processes. The complex behaviors in nature merely provide inspiration in our quest to gain new insights about data. Artificial Intelligence for Humans is a book series meant to teach AI to those readers who lack an extensive mathematical background. The reader only needs knowledge of basic college algebra and computer programming. Additional topics are thoroughly explained. Every chapter also includes a programming example. Examples are currently provided in Java, C#, and Python. Other languages are planned. No knowledge of biology is needed to read this book. With a forward by Dave Snell.

Bootstrapping Machine Learning

Titre : Bootstrapping Machine Learning
Auteur : Louis Dorard
Éditeur : CreateSpace
ISBN-13 : 1500789240
Libération : 2014-08-11

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In an age of overflowing data, Machine Learning and Data Science seem to be all the rage. By analyzing data, computers are able to "learn" and generalize from examples of things happening in the real world. They can make predictions and answer questions such as “How much should I price this product?” and “Which type of document is this?”.Prediction APIs are making Machine Learning accessible to everyone and this book is the first that teaches how to use them. You will learn the possibilities offered by these APIs, how to formulate your own Machine Learning problem, and what are the key concepts to grasp — not how algorithms work, so it doesn't take a university degree to understand.Learn more at

Learning Deep Architectures for AI

Titre : Learning Deep Architectures for AI
Auteur : Yoshua Bengio
Éditeur : Now Publishers Inc
ISBN-13 : 9781601982940
Libération : 2009

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Theoretical results suggest that in order to learn the kind of complicated functions that can represent high-level abstractions (e.g. in vision, language, and other AI-level tasks), one may need deep architectures. Deep architectures are composed of multiple levels of non-linear operations, such as in neural nets with many hidden layers or in complicated propositional formulae re-using many sub-formulae. Searching the parameter space of deep architectures is a difficult task, but learning algorithms such as those for Deep Belief Networks have recently been proposed to tackle this problem with notable success, beating the state-of-the-art in certain areas. This paper discusses the motivations and principles regarding learning algorithms for deep architectures, in particular those exploiting as building blocks unsupervised learning of single-layer models such as Restricted Boltzmann Machines, used to construct deeper models such as Deep Belief Networks.

Servlet JSP and Spring MVC

Titre : Servlet JSP and Spring MVC
Auteur : Budi Kurniawan
Éditeur : Brainy Software Inc
ISBN-13 : 9781771970020
Libération : 2015-01-05

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This book is a tutorial on Servlet, JSP and Spring MVC. Servlet and JSP are two fundamental technologies for developing Java web applications and Spring MVC is a module within Spring Framework that solves common problems in Servlet/JSP application development. The MVC in Spring MVC stands for Model-View-Controller, a design pattern widely used in Graphical User Interface (GUI) development. Spring MVC is one of the most popular web frameworks today and a most sought-after skill. The book is an ideal resource for anyone wanting to learn how to develop Java-based web applications using Servlet, JSP and Spring MVC.