Second-Order Methods for 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 Second-Order Methods for Neural Networks write by Adrian J. Shepherd. This book was released on 2012-12-06. Second-Order Methods for Neural Networks available in PDF, EPUB and Kindle. About This Book This book is about training methods - in particular, fast second-order training methods - for multi-layer perceptrons (MLPs). MLPs (also known as feed-forward neural networks) are the most widely-used class of neural network. Over the past decade MLPs have achieved increasing popularity among scientists, engineers and other professionals as tools for tackling a wide variety of information processing tasks. In common with all neural networks, MLPsare trained (rather than programmed) to carryout the chosen information processing function. Unfortunately, the (traditional' method for trainingMLPs- the well-knownbackpropagation method - is notoriously slow and unreliable when applied to many prac tical tasks. The development of fast and reliable training algorithms for MLPsis one of the most important areas ofresearch within the entire field of neural computing. The main purpose of this book is to bring to a wider audience a range of alternative methods for training MLPs, methods which have proved orders of magnitude faster than backpropagation when applied to many training tasks. The book also addresses the well-known (local minima' problem, and explains ways in which fast training methods can be com bined with strategies for avoiding (or escaping from) local minima. All the methods described in this book have a strong theoretical foundation, drawing on such diverse mathematical fields as classical optimisation theory, homotopic theory and stochastic approximation theory.
Optimization for Machine Learning
Optimization for Machine 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 Optimization for Machine Learning write by Suvrit Sra. This book was released on 2012. Optimization for Machine Learning available in PDF, EPUB and Kindle. An up-to-date account of the interplay between optimization and machine learning, accessible to students and researchers in both communities. The interplay between optimization and machine learning is one of the most important developments in modern computational science. Optimization formulations and methods are proving to be vital in designing algorithms to extract essential knowledge from huge volumes of data. Machine learning, however, is not simply a consumer of optimization technology but a rapidly evolving field that is itself generating new optimization ideas. This book captures the state of the art of the interaction between optimization and machine learning in a way that is accessible to researchers in both fields. Optimization approaches have enjoyed prominence in machine learning because of their wide applicability and attractive theoretical properties. The increasing complexity, size, and variety of today's machine learning models call for the reassessment of existing assumptions. This book starts the process of reassessment. It describes the resurgence in novel contexts of established frameworks such as first-order methods, stochastic approximations, convex relaxations, interior-point methods, and proximal methods. It also devotes attention to newer themes such as regularized optimization, robust optimization, gradient and subgradient methods, splitting techniques, and second-order methods. Many of these techniques draw inspiration from other fields, including operations research, theoretical computer science, and subfields of optimization. The book will enrich the ongoing cross-fertilization between the machine learning community and these other fields, and within the broader optimization community.
Second Order Backpropagation: Efficient Computation of the Hessian Matrix for Neural Networks
Second Order Backpropagation: Efficient Computation of the Hessian Matrix for 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 Second Order Backpropagation: Efficient Computation of the Hessian Matrix for Neural Networks write by International Computer Science Institute. This book was released on 1993. Second Order Backpropagation: Efficient Computation of the Hessian Matrix for Neural Networks available in PDF, EPUB and Kindle. Abstract: "Traditional learning methods for neural networks use some kind of gradient descent in order to determine the network's weights for a given task. Some second order learning algorithms deal with a quadratic approximation of the error function determined from the calculation of the Hessian matrix, and achieve improved convergence rates in many cases. We introduce in this paper second order backpropagation, a method to calculate efficiently the Hessian of a linear network of one- dimensional functions. This technique can be used to get explicit symbolic expressions or numerical approximations of the Hessian and could be used in parallel computers to improve second order learning algorithms for neural networks. It can be of interest also for computer algebra systems."
Neural Networks: Tricks of the Trade
Neural Networks: Tricks of the Trade - 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 Neural Networks: Tricks of the Trade write by Grégoire Montavon. This book was released on 2012-11-14. Neural Networks: Tricks of the Trade available in PDF, EPUB and Kindle. The twenty last years have been marked by an increase in available data and computing power. In parallel to this trend, the focus of neural network research and the practice of training neural networks has undergone a number of important changes, for example, use of deep learning machines. The second edition of the book augments the first edition with more tricks, which have resulted from 14 years of theory and experimentation by some of the world's most prominent neural network researchers. These tricks can make a substantial difference (in terms of speed, ease of implementation, and accuracy) when it comes to putting algorithms to work on real problems.
First-order and Stochastic Optimization Methods for Machine Learning
First-order and Stochastic Optimization Methods for Machine 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 First-order and Stochastic Optimization Methods for Machine Learning write by Guanghui Lan. This book was released on 2020-05-15. First-order and Stochastic Optimization Methods for Machine Learning available in PDF, EPUB and Kindle. This book covers not only foundational materials but also the most recent progresses made during the past few years on the area of machine learning algorithms. In spite of the intensive research and development in this area, there does not exist a systematic treatment to introduce the fundamental concepts and recent progresses on machine learning algorithms, especially on those based on stochastic optimization methods, randomized algorithms, nonconvex optimization, distributed and online learning, and projection free methods. This book will benefit the broad audience in the area of machine learning, artificial intelligence and mathematical programming community by presenting these recent developments in a tutorial style, starting from the basic building blocks to the most carefully designed and complicated algorithms for machine learning.
