Introduction to Deep Learning: From Logical Calculus to Artificial Intelligence

dc.contributor.author	Skansi, Sandro	en_US
dc.date.accessioned	2020-08-05T07:12:41Z
dc.date.available	2020-08-05T07:12:41Z
dc.date.issued	2018	en_US
dc.identifier.isbn	978-3-319-73003-5	en_US
dc.identifier.isbn	978-3-319-73004-2	en_US
dc.identifier.other	HPU2164645	en_US
dc.identifier.uri	https://lib.hpu.edu.vn/handle/123456789/33694
dc.description.abstract	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.	en_US
dc.format.extent	196p.	en_US
dc.format.mimetype	application/pdf
dc.language.iso	en	en_US
dc.publisher	Springer	en_US
dc.subject	Deep learning	en_US
dc.subject	Neural networks	en_US
dc.subject	Natural language processing	en_US
dc.subject	Artificial Intelligence	en_US
dc.title	Introduction to Deep Learning: From Logical Calculus to Artificial Intelligence	en_US
dc.type	Book	en_US
dc.size	2,45 MB	en_US
dc.department	Technology	en_US