000			01806 a2200169 4500
020			_a9781119861867
082			_a006.31 _bM366d
100			_aMartinez-Ramon, Manel
245			_aDeep learning _ba practical introduction _cManel Martinez-Ramon, Meenu Ajith and Aswathy Rajendra Kurup
260			_bWiley _c2024 _aHoboken
300			_axxiii, 392p
520			_aIn Deep Learning: A Practical Introduction, a team of distinguished researchers delivers a book complete with coverage of the theoretical and practical elements of deep learning. The book includes extensive examples, end-of-chapter exercises, homework, exam material, and a GitHub repository containing code and data for all provided examples. Combining contemporary deep learning theory with state-of-the-art tools, the chapters are structured to maximize accessibility for both beginning and intermediate students. The authors have included coverage of TensorFlow, Keras, and Pytorch. Readers will also find: • Thorough introductions to deep learning and deep learning tools • Comprehensive explorations of convolutional neural networks, including discussions of their elements, operation, training, and architectures • Practical discussions of recurrent neural networks and non-supervised approaches to deep learning • Fulsome treatments of generative adversarial networks as well as deep Bayesian neural networks Perfect for undergraduate and graduate students studying computer vision, computer science, artificial intelligence, and neural networks, Deep Learning: A Practical Introduction will also benefit practitioners and researchers in the fields of deep learning and machine learning in general.
650			_aDeep learning
700			_aAjith, Meenu
700			_aKurup, Aswathy Rajendra
942			_cBK
999			_c567569 _d567569