000			01675aam a22002411i 4500
001			0000320710
003			0001
005			20240220140115.0
008			191220s2018 sz#a ob 001 0 eng d
015			_aGBB8H0498 _2bnb
020			_a9783319944623 (hardback) _qCUI, JZL
020			_a9783319944630 (eBook)
040			_aGW5XE _beng _cGW5XE _dUk _erda _epn
042			_aukblsr
082	0	4	_a006.32 _223
084			_a006.32 _bAGG-N
100	1		_aAggarwal, Charu C., _eauthor.
245	1	0	_aNeural networks and deep learning _h[Book] : _ba textbook / _cCharu C. Aggarwal.
300			_axxiii, 497 pages : _billustrations (some color). _c26 cm.
365			_a01 _b0.00
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. Applications associated with many different areas like recommender systems, machine translation, image captioning, image classification, reinforcement-learning based gaming, and text analytics are covered. The chapters of this book span three categories:
942			_2ddc _cBK _n0
999			_c485623 _d485623