000			nam a22 7a 4500
999			_c29124 _d29124
008			180727b xxu||||| |||| 00| 0 eng d
020			_a9783319730035
082			_a006.3 _bSKA
100			_aSkansi, Sandro
245			_aIntroduction to deep learning : from logical calculus to artificial intelligence
260			_bSpringer, _c2018 _aSwitzerland:
300			_aix, 191 p. : _bill.; _c24 cm.
365			_aEURO _b44.99
440			_aUndergraduate topics in computer science
520			_aThis 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			_aCoding theory
650			_aData Mining
650			_aPattern Recognition
650			_aNeural Networks
650			_aMathematical Models of Cognitive Processes and Neural Networks
650			_aPattern perception
650			_aCognitive Science
650			_aAutoencoders
650			_aPython Programming
650			_aElman Networks
650			_aWord Embeddings
650			_aXOR Problem
650			_aLinear Programming
650			_aArtificial intelligence
650			_aImage Processing
650			_aComputer Vision
650			_aKnowledge Discovery
650			_aComputer Science
650			_aInformation theory.
650			_aCoding theory.
650			_aMachine learning
942			_2ddc _cBK