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nam a22 7a 4500 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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180727b xxu||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
9783319730035 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
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| Classification number |
006.3 |
| Item number |
SKA |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Skansi, Sandro |
| 245 ## - TITLE STATEMENT |
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| Title |
Introduction to deep learning : from logical calculus to artificial intelligence |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
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| Name of publisher, distributor, etc |
Springer, |
| Date of publication, distribution, etc |
2018 |
| Place of publication, distribution, etc |
Switzerland: |
| 300 ## - PHYSICAL DESCRIPTION |
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| Extent |
ix, 191 p. : |
| Other physical details |
ill.; |
| Dimensions |
24 cm. |
| 365 ## - TRADE PRICE |
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| Price type code |
EURO |
| Price amount |
44.99 |
| 520 ## - SUMMARY, ETC. |
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| Summary, etc |
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 |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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Coding theory |
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Data Mining |
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Pattern Recognition |
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Neural Networks |
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Mathematical Models of Cognitive Processes and Neural Networks |
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Pattern perception |
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Cognitive Science |
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Autoencoders |
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Python Programming |
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Elman Networks |
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Word Embeddings |
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XOR Problem |
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Linear Programming |
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Artificial intelligence |
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Image Processing |
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Computer Vision |
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Knowledge Discovery |
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Computer Science |
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Information theory. |
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Coding theory. |
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Machine learning |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
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| Source of classification or shelving scheme |
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Books |