000			01978nam a22003137a 4500
003			OSt
005			20250225113805.0
008			250224b |||||||| |||| 00| 0 hin d
020			_a9783031296413
040			_aNISER LIBRARY _beng _cNISER LIBRARY
082			_a004.032.26 _bAGG-N
100			_aAggarwal, Charu C.
245			_aNeural networks and deep learning : _ba textbook
250			_a2nd edition
260			_aCham : _bSpringer, _c2023.
300			_axxiv, 529 pages. : _billustrations (128 b/w illustrations, 22 illustrations in colour)
504			_aIncludes bibliographical references and index
520			_aThis book covers both classical and modern models in deep learning. The primary focus is on the theory and algorithms of deep learning. The theory and algorithms of neural networks are particularly important for understanding important concepts, so that one can understand the important design concepts of neural architectures in different applications. Why do neural networks work? When do they work better than off-the-shelf machine-learning models? When is depth useful? Why is training neural networks so hard? What are the pitfalls? The book is also rich in discussing different applications in order to give the practitioner a flavor of how neural architectures are designed for different types of problems. Deep learning methods for various data domains, such as text, images, and graphs are presented in detail.
650			_aNeural networks (Computer science)
650			_aDeep learning
650			_aMachine learning
650			_aArtificial intelligence
650			_aPre-trained language models
650			_aReinforcement learning
650			_aRecurrent neural networks
856			_3Table of content _uhttps://link.springer.com/content/pdf/bfm:978-3-031-29642-0/1
856			_3Reviews _uhttps://www.goodreads.com/book/show/143376276-neural-networks-and-deep-learning?ref=nav_sb_ss_1_13#CommunityReviews
942			_2udc _cBK
999			_c35751 _d35751