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In CLASSICAL AND MODERN REGRESSION WITH APPLICATIONS, Second Edition, Raymond H.
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You can download Neural Networks and Deep Learning ebook for free in PDF format (7.3 MB). Unlike static PDF Classical and Modern Regression with Applications solution manuals or printed answer keys, our experts show you how to solve each problem step-by-step.
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This open book is licensed under a Creative Commons License (CC BY). Where possible, an application-centric view is highlighted in order to provide an understanding of the practical uses of each class of techniques. Numerous exercises are available along with a solution manual to aid in classroom teaching. The book is written for graduate students, researchers, and practitioners. 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. Chapters 5 and 6 present radial-basis function (RBF) networks and restricted Boltzmann machines.Īdvanced topics in neural networks: Chapters 7 and 8 discuss recurrent neural networks and convolutional neural networks. These methods are studied together with recent feature engineering methods like word2vec.įundamentals of neural networks: A detailed discussion of training and regularization is provided in Chapters 3 and 4. Support vector machines, linear/logistic regression, singular value decomposition, matrix factorization, and recommender systems are shown to be 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. The basics of neural networks: Many traditional machine learning models can be understood as special cases of neural networks. Classical and Modern Regression with Applications (Duxbury Classic) by Raymond H. The chapters of this book span three categories:
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Book DescriptionThis book covers both classical and modern models in deep learning.