Mathematical Perspectives on Neural Networks - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Mathematical Perspectives on Neural Networks write by Paul Smolensky. This book was released on 2013-05-13. Mathematical Perspectives on Neural Networks available in PDF, EPUB and Kindle. Recent years have seen an explosion of new mathematical results on learning and processing in neural networks. This body of results rests on a breadth of mathematical background which even few specialists possess. In a format intermediate between a textbook and a collection of research articles, this book has been assembled to present a sample of these results, and to fill in the necessary background, in such areas as computability theory, computational complexity theory, the theory of analog computation, stochastic processes, dynamical systems, control theory, time-series analysis, Bayesian analysis, regularization theory, information theory, computational learning theory, and mathematical statistics. Mathematical models of neural networks display an amazing richness and diversity. Neural networks can be formally modeled as computational systems, as physical or dynamical systems, and as statistical analyzers. Within each of these three broad perspectives, there are a number of particular approaches. For each of 16 particular mathematical perspectives on neural networks, the contributing authors provide introductions to the background mathematics, and address questions such as: * Exactly what mathematical systems are used to model neural networks from the given perspective? * What formal questions about neural networks can then be addressed? * What are typical results that can be obtained? and * What are the outstanding open problems? A distinctive feature of this volume is that for each perspective presented in one of the contributed chapters, the first editor has provided a moderately detailed summary of the formal results and the requisite mathematical concepts. These summaries are presented in four chapters that tie together the 16 contributed chapters: three develop a coherent view of the three general perspectives -- computational, dynamical, and statistical; the other assembles these three perspectives into a unified overview of the neural networks field.
Mathematical Perspectives on Neural Networks
Mathematical Perspectives on Neural Networks - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Mathematical Perspectives on Neural Networks write by Paul Smolensky. This book was released on 1996-05. Mathematical Perspectives on Neural Networks available in PDF, EPUB and Kindle.
Discrete Mathematics of Neural Networks
Discrete Mathematics of Neural Networks - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Discrete Mathematics of Neural Networks write by Martin Anthony. This book was released on 2001-01-01. Discrete Mathematics of Neural Networks available in PDF, EPUB and Kindle. This concise, readable book provides a sampling of the very large, active, and expanding field of artificial neural network theory. It considers select areas of discrete mathematics linking combinatorics and the theory of the simplest types of artificial neural networks. Neural networks have emerged as a key technology in many fields of application, and an understanding of the theories concerning what such systems can and cannot do is essential. Some classical results are presented with accessible proofs, together with some more recent perspectives, such as those obtained by considering decision lists. In addition, probabilistic models of neural network learning are discussed. Graph theory, some partially ordered set theory, computational complexity, and discrete probability are among the mathematical topics involved. Pointers to further reading and an extensive bibliography make this book a good starting point for research in discrete mathematics and neural networks.
Mathematical Methods for Neural Network Analysis and Design
Mathematical Methods for Neural Network Analysis and Design - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Mathematical Methods for Neural Network Analysis and Design write by Richard M. Golden. This book was released on 1996. Mathematical Methods for Neural Network Analysis and Design available in PDF, EPUB and Kindle. For convenience, many of the proofs of the key theorems have been rewritten so that the entire book uses a relatively uniform notion.
Hands-On Mathematics for Deep Learning
Hands-On Mathematics for Deep Learning - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Hands-On Mathematics for Deep Learning write by Jay Dawani. This book was released on 2020-06-12. Hands-On Mathematics for Deep Learning available in PDF, EPUB and Kindle. A comprehensive guide to getting well-versed with the mathematical techniques for building modern deep learning architectures Key FeaturesUnderstand linear algebra, calculus, gradient algorithms, and other concepts essential for training deep neural networksLearn the mathematical concepts needed to understand how deep learning models functionUse deep learning for solving problems related to vision, image, text, and sequence applicationsBook Description Most programmers and data scientists struggle with mathematics, having either overlooked or forgotten core mathematical concepts. This book uses Python libraries to help you understand the math required to build deep learning (DL) models. You'll begin by learning about core mathematical and modern computational techniques used to design and implement DL algorithms. This book will cover essential topics, such as linear algebra, eigenvalues and eigenvectors, the singular value decomposition concept, and gradient algorithms, to help you understand how to train deep neural networks. Later chapters focus on important neural networks, such as the linear neural network and multilayer perceptrons, with a primary focus on helping you learn how each model works. As you advance, you will delve into the math used for regularization, multi-layered DL, forward propagation, optimization, and backpropagation techniques to understand what it takes to build full-fledged DL models. Finally, you’ll explore CNN, recurrent neural network (RNN), and GAN models and their application. By the end of this book, you'll have built a strong foundation in neural networks and DL mathematical concepts, which will help you to confidently research and build custom models in DL. What you will learnUnderstand the key mathematical concepts for building neural network modelsDiscover core multivariable calculus conceptsImprove the performance of deep learning models using optimization techniquesCover optimization algorithms, from basic stochastic gradient descent (SGD) to the advanced Adam optimizerUnderstand computational graphs and their importance in DLExplore the backpropagation algorithm to reduce output errorCover DL algorithms such as convolutional neural networks (CNNs), sequence models, and generative adversarial networks (GANs)Who this book is for This book is for data scientists, machine learning developers, aspiring deep learning developers, or anyone who wants to understand the foundation of deep learning by learning the math behind it. Working knowledge of the Python programming language and machine learning basics is required.
