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Deep Learning
  • Language: en
  • Pages: 800

Deep Learning

  • Type: Book
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  • Published: 2016-11-18
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  • Publisher: MIT Press

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.

Deep Learning
  • Language: en
  • Pages: 800

Deep Learning

  • Type: Book
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  • Published: 2016-11-10
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  • Publisher: MIT Press

"Written by three experts in the field, Deep Learning is the only comprehensive book on the subject." -- Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of...

L'apprentissage profond
  • Language: fr
  • Pages: 770

L'apprentissage profond

Le livre de chevet de Elon Musk. Écrit par trois experts dans le domaine, Deep Learning est le seul livre complet sur le sujet. Il fournit une perspective générale et des préliminaires mathématiques indispensables aux ingénieurs en logiciel et aux étudiants qui entrent sur le terrain, et sert de référence aux autorités. Elon Musk, cofondateur et PDG de Tesla et SpaceXstudents L'apprentissage profond (ou deep learning) est un apprentissage automatique qui permet à l'ordinateur d'apprendre par l'expérience et de comprendre le monde en termes de hiérarchie de concepts. Parce que l'ordinateur recueille des connaissances à partir de l'expérience, il n'est pas nécessaire qu'un opé...

深層学習
  • Language: ja
  • Pages: 600

深層学習

  • Type: Book
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  • Published: 2018-02-28
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  • Publisher: Unknown

深層学習の世界的名著、ついに刊行

Feature Engineering for Machine Learning and Data Analytics
  • Language: en
  • Pages: 400

Feature Engineering for Machine Learning and Data Analytics

  • Type: Book
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  • Published: 2018-03-14
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  • Publisher: CRC Press

Feature engineering plays a vital role in big data analytics. Machine learning and data mining algorithms cannot work without data. Little can be achieved if there are few features to represent the underlying data objects, and the quality of results of those algorithms largely depends on the quality of the available features. Feature Engineering for Machine Learning and Data Analytics provides a comprehensive introduction to feature engineering, including feature generation, feature extraction, feature transformation, feature selection, and feature analysis and evaluation. The book presents key concepts, methods, examples, and applications, as well as chapters on feature engineering for majo...

The Economics of Artificial Intelligence
  • Language: en
  • Pages: 648

The Economics of Artificial Intelligence

Advances in artificial intelligence (AI) highlight the potential of this technology to affect productivity, growth, inequality, market power, innovation, and employment. This volume seeks to set the agenda for economic research on the impact of AI. It covers four broad themes: AI as a general purpose technology; the relationships between AI, growth, jobs, and inequality; regulatory responses to changes brought on by AI; and the effects of AI on the way economic research is conducted. It explores the economic influence of machine learning, the branch of computational statistics that has driven much of the recent excitement around AI, as well as the economic impact of robotics and automation a...

Handbook on Neural Information Processing
  • Language: en
  • Pages: 538

Handbook on Neural Information Processing

This handbook presents some of the most recent topics in neural information processing, covering both theoretical concepts and practical applications. The contributions include: Deep architectures Recurrent, recursive, and graph neural networks Cellular neural networks Bayesian networks Approximation capabilities of neural networks Semi-supervised learning Statistical relational learning Kernel methods for structured data Multiple classifier systems Self organisation and modal learning Applications to content-based image retrieval, text mining in large document collections, and bioinformatics This book is thought particularly for graduate students, researchers and practitioners, willing to deepen their knowledge on more advanced connectionist models and related learning paradigms.

Challenges in Representation Learning: A Report on Three Machine Learning Contests
  • Language: en

Challenges in Representation Learning: A Report on Three Machine Learning Contests

  • Type: Book
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  • Published: 2015
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  • Publisher: Unknown

Abstract: The ICML 2013 Workshop on Challenges in Representation Learning 1 focused on three challenges: the black box learning challenge, the facial expression recognition challenge, and the multimodal learning challenge. We describe the datasets created for these challenges and summarize the results of the competitions. We provide suggestions for organizers of future challenges and some comments on what kind of knowledge can be gained from machine learning competitions.

Introduction to Machine Learning
  • Language: en
  • Pages: 640

Introduction to Machine Learning

  • Type: Book
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  • Published: 2014-08-29
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  • Publisher: MIT Press

The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Many successful applications of machine learning exist already, including systems that analyze past sales data to predict customer behavior, optimize robot behavior so that a task can be completed using minimum resources, and extract knowledge from bioinformatics data. Introduction to Machine Learning is a comprehensive textbook on the subject, covering a broad array of topics not usually included in introductory machine learning texts. Subjects include supervised learning; Bayesian decision theory; parametric, semi-parametric, and nonparametric methods; multivariate analysis...

Computer Vision
  • Language: en
  • Pages: 580

Computer Vision

A modern treatment focusing on learning and inference, with minimal prerequisites, real-world examples and implementable algorithms.