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Introduction to Deep Learning : From Logical Calculus to Artificial Intelligence - pocketboek

2018, ISBN: 3319730037

[EAN: 9783319730035], Neubuch, [SC: 0.0], [PU: Springer International Publishing], BILDBEARBEITUNG; BILDVERARBEITUNG; GRAFIK (EDV) / DATA MINING (EDV); DATENVERARBEITUNG DATENVERSCHLÜSSEL… Meer...

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Sandro Skansi:

Introduction to Deep Learning - pocketboek

2018, ISBN: 3319730037

[EAN: 9783319730035], Neubuch, [PU: Springer International Publishing Feb 2018], BILDBEARBEITUNG; BILDVERARBEITUNG; GRAFIK (EDV) / DATA MINING (EDV); DATENVERARBEITUNG DATENVERSCHLÜSSELUN… Meer...

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Sandro Skansi:
Introduction to Deep Learning - pocketboek

2015

ISBN: 9783319730035

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Skansi, Sandro:
Introduction to Deep Learning - pocketboek

2018, ISBN: 9783319730035

[ED: Softcover], [PU: Springer / Springer International Publishing / Springer, Berlin], This textbook presents a concise, accessible and engaging first introduction to deep learning, offe… Meer...

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Skansi, Sandro:
Introduction to Deep Learning From Logical Calculus to Artificial Intelligence - nieuw boek

2018, ISBN: 3319730037

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Bibliografische gegevens van het best passende boek

Bijzonderheden over het boek
Introduction to Deep Learning: From Logical Calculus to Artificial Intelligence (Undergraduate Topics in Computer Science)

This textbook presents a concise, accessible and engaging first introduction to deep learning, offering a wide range of connectionist models which represent the current state-of-the-art. The text explores the most popular algorithms and architectures in a simple and intuitive style, explaining the mathematical derivations in a step-by-step manner. The content coverage includes convolutional networks, LSTMs, Word2vec, RBMs, DBNs, neural Turing machines, memory networks and autoencoders. Numerous examples in working Python code are provided throughout the book, and the code is also supplied separately at an accompanying website.

Topics and features: introduces the fundamentals of machine learning, and the mathematical and computational prerequisites for deep learning; discusses feed-forward neural networks, and explores the modifications to these which can be applied to any neural network; examines convolutional neural networks, and the recurrent connections to a feed-forward neural network; describes the notion of distributed representations, the concept of the autoencoder, and the ideas behind language processing with deep learning; presents a brief history of artificial intelligence and neural networks, and reviews interesting open research problems in deep learning and connectionism.

This clearly written and lively primer on deep learning is essential reading for graduate and advanced undergraduate students of computer science, cognitive science and mathematics, as well as fields such as linguistics, logic, philosophy, and psychology.

Gedetalleerde informatie over het boek. - Introduction to Deep Learning: From Logical Calculus to Artificial Intelligence (Undergraduate Topics in Computer Science)


EAN (ISBN-13): 9783319730035
ISBN (ISBN-10): 3319730037
Gebonden uitgave
pocket book
Verschijningsjaar: 2018
Uitgever: Springer

Boek bevindt zich in het datenbestand sinds 2017-12-27T14:59:26+01:00 (Amsterdam)
Detailpagina laatst gewijzigd op 2024-02-21T03:36:14+01:00 (Amsterdam)
ISBN/EAN: 9783319730035

ISBN - alternatieve schrijfwijzen:
3-319-73003-7, 978-3-319-73003-5
alternatieve schrijfwijzen en verwante zoekwoorden:
Auteur van het boek: turing, skansi
Titel van het boek: deep learning, calculus, logical, introduction, computer, topics artificial intelligence


Gegevens van de uitgever

Auteur: Sandro Skansi
Titel: Undergraduate Topics in Computer Science; Introduction to Deep Learning - From Logical Calculus to Artificial Intelligence
Uitgeverij: Springer; Springer International Publishing
191 Bladzijden
Verschijningsjaar: 2018-02-15
Cham; CH
Gedrukt / Gemaakt in
Gewicht: 0,454 kg
Taal: Engels
53,49 € (DE)
54,99 € (AT)
59,00 CHF (CH)
POD
XIII, 191 p. 38 illus.

BC; Machine Learning; Hardcover, Softcover / Informatik, EDV/Informatik; Maschinelles Lernen; Verstehen; Deep learning; Neural networks; Pattern recognition; Natural language processing; Autoencoders; Pattern Recognition; Mathematical Models of Cognitive Processes and Neural Networks; Coding and Information Theory; Machine Learning; Automated Pattern Recognition; Mathematical Models of Cognitive Processes and Neural Networks; Coding and Information Theory; Mustererkennung; Mathematische Modellierung; Kodierungstheorie und Verschlüsselung (Kryptologie); Informationstheorie; EA

This textbook presents a concise, accessible and engaging first introduction to deep learning, offering a wide range of connectionist models which represent the current state-of-the-art. The text explores the most popular algorithms and architectures in a simple and intuitive style, explaining the mathematical derivations in a step-by-step manner. The content coverage includes convolutional networks, LSTMs, Word2vec, RBMs, DBNs, neural Turing machines, memory networks and autoencoders. Numerous examples in working Python code are provided throughout the book, and the code is also supplied separately at an accompanying website.

This clearly written and lively primer on deep learning is essential reading for graduate and advanced undergraduate students of computer science, cognitive science and mathematics, as well as fields such as linguistics, logic, philosophy, and psychology.

From Logic to Cognitive Science

Mathematical and Computational Prerequisites

Machine Learning Basics

Feed-forward Neural Networks

Modifications and Extensions to a Feed-forward Neural Network

Convolutional Neural Networks

Recurrent Neural Networks

Autoencoders

Neural Language Models

An Overview of Different Neural Network Architectures

Conclusion

Dr. Sandro Skansi

This textbook presents a concise, accessible and engaging first introduction to deep learning, offering a wide range of connectionist models which represent the current state-of-the-art. The text explores the most popular algorithms and architectures in a simple and intuitive style, explaining the mathematical derivations in a step-by-step manner. The content coverage includes convolutional networks, LSTMs, Word2vec, RBMs, DBNs, neural Turing machines, memory networks and autoencoders. Numerous examples in working Python code are provided throughout the book, and the code is also supplied separately at an accompanying website.

is an Assistant Professor of Logic at the University of Zagreb and Lecturer in Data Science at University College Algebra, Zagreb, Croatia.

Topics and features: Dr. Sandro Skansi

Offers a welcome clarity of expression, maintaining mathematical rigor yet presenting the ideas in an intuitive and colourful manner

Includes references to open problems studied in other disciplines, enabling the reader to pursue these topics on their own, armed with the tools learned from the book

Presents an accessible style and interdisciplinary approach, with a vivid and lively exposition supported by numerous examples, connected ideas, and historical remarks



Andere boeken die eventueel grote overeenkomsten met dit boek kunnen hebben:

Laatste soortgelijke boek:
9780262039512 Introduction to Deep Learning (The MIT Press) (Charniak, Eugene)


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