| 000 | 02450nam a22002537a 4500 | ||
|---|---|---|---|
| 008 | 230201s2018 |||||||| |||| 00| 0 eng d | ||
| 020 | _a9783319730035 | ||
| 022 | _l9783319730035 | ||
| 041 | _aeng | ||
| 082 |
_a006.31 _bSKA |
||
| 100 |
_aSkansi, Sandro _9673678 _eAU |
||
| 245 | _aIntroduction to Deep Learning from Logical Calculus to Artificial Intelligence | ||
| 260 |
_aCham, Switzerland : _bSpringer, _cc2018 |
||
| 300 |
_aXIII, 191 p. _b: ill |
||
| 490 | _aUndergraduate Topics in Computer Science | ||
| 504 | _aYY | ||
| 520 | _aSUMMARY: 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. | ||
| 650 | 0 |
_aCoding Theory _9987 |
|
| 650 | 0 |
_9156028 _aInformation Theory |
|
| 650 | 0 |
_aMachine Learning _9845 |
|
| 856 |
_yWEB LINK _uhttps://link.springer.com/book/10.1007/978-3-319-73004-2#toc |
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| 856 |
_yTOC _uhttps://eaklibrary.neduet.edu.pk:8443/catalog/bk/books/toc/9783319730035.pdf |
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| 942 |
_2ddc _cBOO |
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| 999 |
_c701230 _d701230 |
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