000			02137nam a22002177a 4500
999			_c1566 _d1566
005			20220204104335.0
008			220204b ||||| |||| 00| 0 eng d
020			_a9783319944623
082			_a006.32 _bAGG
100			_aAggarwal, Charu C. _94493
245			_aNeural networks and deep learning: a textbook
260			_bSpringer _aSwitzerland _c2018
300			_axxi, 497 p.
365			_aEURO _b59.99
520			_aIntroduction This book covers both classical and modern models in deep learning. The chapters of this book span three categories: The basics of neural networks: Many traditional machine learning models can be understood as special cases of neural networks. An emphasis is placed in the first two chapters on understanding the relationship between traditional machine learning and neural networks. Support vector machines, linear/logistic regression, singular value decomposition, matrix factorization, and recommender systems are shown to be special cases of neural networks. These methods are studied together with recent feature engineering methods like word2vec. Fundamentals of neural networks: A detailed discussion of training and regularization is provided in Chapters 3 and 4. Chapters 5 and 6 present radial-basis function (RBF) networks and restricted Boltzmann machines. Advanced topics in neural networks: Chapters 7 and 8 discuss recurrent neural networks and convolutional neural networks. Several advanced topics like deep reinforcement learning, neural Turing machines, Kohonen self-organizing maps, and generative adversarial networks are introduced in Chapters 9 and 10. The book is written for graduate students, researchers, and practitioners. Numerous exercises are available along with a solution manual to aid in classroom teaching. Where possible, an application-centric view is highlighted in order to provide an understanding of the practical uses of each class of techniques.
650			_aNeural networks (Computer science) _92344
650			_aMachine learning _92343
650			_aArtificial intelligence _91478
650			_aComputer science _91018
942			_2ddc _cBK