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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...

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.

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

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...

L'apprentissage profond
  • Language: fr
  • Pages: 768

L'apprentissage profond

  • Type: Book
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  • Published: 2018-10-18
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  • Publisher: Unknown

None

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...

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.

Deep Learning. Das umfassende Handbuch
  • Language: de
  • Pages: 912

Deep Learning. Das umfassende Handbuch

• Mathematische Grundlagen für Machine und Deep Learning • Umfassende Behandlung zeitgemäßer Verfahren: tiefe Feedforward-Netze, Regularisierung, Performance-Optimierung sowie CNNs, Rekurrente und Rekursive Neuronale Netze • Zukunftsweisende Deep-Learning-Ansätze sowie von Ian Goodfellow neu entwickelte Konzepte wie Generative Adversarial Networks Deep Learning ist ein Teilbereich des Machine Learnings und versetzt Computer in die Lage, aus Erfahrungen zu lernen. Dieses Buch behandelt umfassend alle Aspekte, die für den Einsatz und die Anwendung von Deep Learning eine Rolle spielen: In Teil I erläutern die Autoren die mathematischen Grundlagen für Künstliche Intelligenz, Neuron...

Machine Learning Solutions
  • Language: en
  • Pages: 566

Machine Learning Solutions

Practical, hands-on solutions in Python to overcome any problem in Machine Learning Key Features Master the advanced concepts, methodologies, and use cases of machine learning Build ML applications for analytics, NLP and computer vision domains Solve the most common problems in building machine learning models Book Description Machine learning (ML) helps you find hidden insights from your data without the need for explicit programming. This book is your key to solving any kind of ML problem you might come across in your job. You’ll encounter a set of simple to complex problems while building ML models, and you'll not only resolve these problems, but you’ll also learn how to build project...

Advances in Neural Information Processing Systems 15
  • Language: en
  • Pages: 1687

Advances in Neural Information Processing Systems 15

  • Type: Book
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  • Published: 2003
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  • Publisher: MIT Press

Proceedings of the 2002 Neural Information Processing Systems Conference. The annual Neural Information Processing (NIPS) meeting is the flagship conference on neural computation. The conference draws a diverse group of attendees--physicists, neuroscientists, mathematicians, statisticians, and computer scientists--and the presentations are interdisciplinary, with contributions in algorithms, learning theory, cognitive science, neuroscience, vision, speech and signal processing, reinforcement learning and control, implementations, and applications. Only about thirty percent of the papers submitted are accepted for presentation at NIPS, so the quality is exceptionally high. This volume contains all the papers presented at the 2002 conference.