000			01757nam a22001697a 4500
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