Introduction to Deep Learning : From Logical Calculus to Artificial Intelligence
Mentés helye:
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| Különgyűjtemény: | e-book |
| Formátum: | könyv |
| Nyelv: | angol |
| Megjelenés: |
Cham : Springer International Publishing,
2018
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| Sorozat: | Undergraduate Topics in Computer Science, ISSN 1863-7310 |
| Tárgyszavak: | |
| Online elérés: | http://doi.org/10.1007/978-3-319-73004-2 |
| Címkék: |
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opac-EUL01-000977663 |
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| collection |
e-book |
| institution |
L_042 EUL01 |
| spelling |
Skansi, Sandro Introduction to Deep Learning From Logical Calculus to Artificial Intelligence by Sandro Skansi Cham Springer International Publishing 2018 XIII, 191 p. 38 illusztrált online forrás szöveg txt rdacontent számítógépes c rdamedia távoli hozzáférés cr rdacarrier szövegfájl PDF rda Undergraduate Topics in Computer Science 1863-7310 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. 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. Dr. Sandro Skansi is an Assistant Professor of Logic at the University of Zagreb and Lecturer in Data Science at University College Algebra, Zagreb, Croatia. Nyomtatott kiadás: ISBN 9783319730035 Nyomtatott kiadás: ISBN 9783319730059 Az e-könyvek a teljes ELTE IP-tartományon belül online elérhetők. könyv e-book gépi tanulás mesterséges intelligencia informatika Machine learning. Optical pattern recognition. Coding theory. elektronikus könyv SpringerLink (Online service) közreadó testület Undergraduate topics in computer science Online változat http://doi.org/10.1007/978-3-319-73004-2 Cham Springer International Publishing Imprint: Springer 2018 EUL01 |
| language |
English |
| format |
Book |
| author |
Skansi, Sandro |
| spellingShingle |
Skansi, Sandro Introduction to Deep Learning : From Logical Calculus to Artificial Intelligence Undergraduate Topics in Computer Science, ISSN 1863-7310 gépi tanulás -- mesterséges intelligencia -- informatika Machine learning. Optical pattern recognition. Coding theory. elektronikus könyv |
| author_facet |
Skansi, Sandro SpringerLink (Online service), közreadó testület |
| author_corporate |
SpringerLink (Online service), közreadó testület |
| author_sort |
Skansi, Sandro |
| title |
Introduction to Deep Learning : From Logical Calculus to Artificial Intelligence |
| title_sub |
From Logical Calculus to Artificial Intelligence |
| title_short |
Introduction to Deep Learning |
| title_full |
Introduction to Deep Learning From Logical Calculus to Artificial Intelligence by Sandro Skansi |
| title_fullStr |
Introduction to Deep Learning From Logical Calculus to Artificial Intelligence by Sandro Skansi |
| title_full_unstemmed |
Introduction to Deep Learning From Logical Calculus to Artificial Intelligence by Sandro Skansi |
| title_auth |
Introduction to Deep Learning From Logical Calculus to Artificial Intelligence |
| title_sort |
introduction to deep learning from logical calculus to artificial intelligence |
| series |
Undergraduate Topics in Computer Science, ISSN 1863-7310 |
| series2 |
Undergraduate topics in computer science |
| publishDate |
2018 |
| publishDateSort |
2018 |
| physical |
XIII, 191 p. 38 illusztrált : online forrás |
| isbn |
978-3-319-73004-2 |
| issn |
1863-7310 |
| callnumber-first |
Q - Science |
| callnumber-subject |
Q - General Science |
| callnumber-label |
Q325 |
| callnumber-raw |
14622 |
| callnumber-search |
14622 |
| topic |
gépi tanulás -- mesterséges intelligencia -- informatika Machine learning. Optical pattern recognition. Coding theory. elektronikus könyv |
| topic_facet |
gépi tanulás -- mesterséges intelligencia -- informatika Machine learning. Optical pattern recognition. Coding theory. elektronikus könyv gépi tanulás Machine learning. Optical pattern recognition. Coding theory. mesterséges intelligencia informatika |
| url |
http://doi.org/10.1007/978-3-319-73004-2 |
| illustrated |
Illustrated |
| dewey-hundreds |
000 - Computer science, information & general works |
| dewey-tens |
000 - Computer science, knowledge & systems |
| dewey-ones |
006 - Special computer methods |
| dewey-full |
006.31 |
| dewey-sort |
16.31 |
| dewey-raw |
006.31 |
| dewey-search |
006.31 |
| first_indexed |
2023-12-26T23:19:16Z |
| last_indexed |
2023-12-29T19:19:18Z |
| recordtype |
opac |
| publisher |
Cham : Springer International Publishing |
| _version_ |
1786641341239787521 |
| score |
13,364243 |
| generalnotes |
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. Dr. Sandro Skansi is an Assistant Professor of Logic at the University of Zagreb and Lecturer in Data Science at University College Algebra, Zagreb, Croatia. |