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005 20240229160614.0
008 240229b |||||||| |||| 00| 0 eng d
020 _a9783319944623
082 _a006.3
_bAGG
100 _aCharu C. Aggarwal
245 _a Neural networks and deep learning : a textbook
_ba textbook
_cCharu C. Aggarwal
260 _aCham, Switzerland,
_bSpringer,
_c2018
300 _axxiii, 497 pages
_c27 cm
505 _t1 An Introduction to Neural Networks.- 2 Machine Learning with Shallow Neural Networks.- 3 Training Deep Neural Networks.- 4 Teaching Deep Learners to Generalize.- 5 Radical Basis Function Networks.- 6 Restricted Boltzmann Machines.- 7 Recurrent Neural Networks.- 8 Convolutional Neural Networks.- 9 Deep Reinforcement Learning.- 10 Advanced Topics in Deep Learning.
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
942 _2ddc
_cBK
999 _c2105
_d2105