Neural Networks and Deep Learning / Charu C. Aggarwal
Publication year: 2018
ISBN: 978-3-319-94463-0
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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.
Subject: Machine learning, Neural Networks, Computer, Neural networks (Computer science), Apprentissage automatique, Réseaux neuronaux (Informatique), Deep Learning, Machine Learning, Radial Basis Function Networks, Restricted Boltzmann Machines, Recurrent Neural Networks, Convolutional Neural Networks, Neural networks perceptron, Deep reinforcement learning, word2vec, Autoencoder, Logistic regression, Dropout, Pretraining, Backpropagation, Conjugate gradient-descent, Adam, RMSProp, Kohonean self-organizaing map, Generative adversarial networks
