Bayesian Learning For Neural Networks PDF Download
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| Author | : Radford M. Neal |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 194 |
| Release | : 2012-12-06 |
| Genre | : Mathematics |
| ISBN | : 1461207452 |
Download Bayesian Learning for Neural Networks Book in PDF, ePub and Kindle
Artificial "neural networks" are widely used as flexible models for classification and regression applications, but questions remain about how the power of these models can be safely exploited when training data is limited. This book demonstrates how Bayesian methods allow complex neural network models to be used without fear of the "overfitting" that can occur with traditional training methods. Insight into the nature of these complex Bayesian models is provided by a theoretical investigation of the priors over functions that underlie them. A practical implementation of Bayesian neural network learning using Markov chain Monte Carlo methods is also described, and software for it is freely available over the Internet. Presupposing only basic knowledge of probability and statistics, this book should be of interest to researchers in statistics, engineering, and artificial intelligence.
| Author | : Richard E. Neapolitan |
| Publisher | : Prentice Hall |
| Total Pages | : 704 |
| Release | : 2004 |
| Genre | : Computers |
| ISBN | : |
Download Learning Bayesian Networks Book in PDF, ePub and Kindle
In this first edition book, methods are discussed for doing inference in Bayesian networks and inference diagrams. Hundreds of examples and problems allow readers to grasp the information. Some of the topics discussed include Pearl's message passing algorithm, Parameter Learning: 2 Alternatives, Parameter Learning r Alternatives, Bayesian Structure Learning, and Constraint-Based Learning. For expert systems developers and decision theorists.
| Author | : David Barber |
| Publisher | : Cambridge University Press |
| Total Pages | : 739 |
| Release | : 2012-02-02 |
| Genre | : Computers |
| ISBN | : 0521518148 |
Download Bayesian Reasoning and Machine Learning Book in PDF, ePub and Kindle
A practical introduction perfect for final-year undergraduate and graduate students without a solid background in linear algebra and calculus.
| Author | : Olivier Bousquet |
| Publisher | : Springer |
| Total Pages | : 249 |
| Release | : 2011-03-22 |
| Genre | : Computers |
| ISBN | : 3540286500 |
Download Advanced Lectures on Machine Learning Book in PDF, ePub and Kindle
Machine Learning has become a key enabling technology for many engineering applications, investigating scientific questions and theoretical problems alike. To stimulate discussions and to disseminate new results, a summer school series was started in February 2002, the documentation of which is published as LNAI 2600. This book presents revised lectures of two subsequent summer schools held in 2003 in Canberra, Australia, and in Tübingen, Germany. The tutorial lectures included are devoted to statistical learning theory, unsupervised learning, Bayesian inference, and applications in pattern recognition; they provide in-depth overviews of exciting new developments and contain a large number of references. Graduate students, lecturers, researchers and professionals alike will find this book a useful resource in learning and teaching machine learning.
| Author | : Herbert K. H. Lee |
| Publisher | : SIAM |
| Total Pages | : 106 |
| Release | : 2004-01-01 |
| Genre | : Mathematics |
| ISBN | : 9780898718423 |
Download Bayesian Nonparametrics via Neural Networks Book in PDF, ePub and Kindle
Bayesian Nonparametrics via Neural Networks is the first book to focus on neural networks in the context of nonparametric regression and classification, working within the Bayesian paradigm. Its goal is to demystify neural networks, putting them firmly in a statistical context rather than treating them as a black box. This approach is in contrast to existing books, which tend to treat neural networks as a machine learning algorithm instead of a statistical model. Once this underlying statistical model is recognized, other standard statistical techniques can be applied to improve the model. The Bayesian approach allows better accounting for uncertainty. This book covers uncertainty in model choice and methods to deal with this issue, exploring a number of ideas from statistics and machine learning. A detailed discussion on the choice of prior and new noninformative priors is included, along with a substantial literature review. Written for statisticians using statistical terminology, Bayesian Nonparametrics via Neural Networks will lead statisticians to an increased understanding of the neural network model and its applicability to real-world problems.
| Author | : Martin J. Wainwright |
| Publisher | : Now Publishers Inc |
| Total Pages | : 324 |
| Release | : 2008 |
| Genre | : Computers |
| ISBN | : 1601981848 |
Download Graphical Models, Exponential Families, and Variational Inference Book in PDF, ePub and Kindle
The core of this paper is a general set of variational principles for the problems of computing marginal probabilities and modes, applicable to multivariate statistical models in the exponential family.
| Author | : Shinichi Nakajima |
| Publisher | : Cambridge University Press |
| Total Pages | : 561 |
| Release | : 2019-07-11 |
| Genre | : Computers |
| ISBN | : 1107076153 |
Download Variational Bayesian Learning Theory Book in PDF, ePub and Kindle
This introduction to the theory of variational Bayesian learning summarizes recent developments and suggests practical applications.
| Author | : David Saad |
| Publisher | : Cambridge University Press |
| Total Pages | : 412 |
| Release | : 2009-07-30 |
| Genre | : Computers |
| ISBN | : 9780521117913 |
Download On-Line Learning in Neural Networks Book in PDF, ePub and Kindle
On-line learning is one of the most commonly used techniques for training neural networks. Though it has been used successfully in many real-world applications, most training methods are based on heuristic observations. The lack of theoretical support damages the credibility as well as the efficiency of neural networks training, making it hard to choose reliable or optimal methods. This book presents a coherent picture of the state of the art in the theoretical analysis of on-line learning. An introduction relates the subject to other developments in neural networks and explains the overall picture. Surveys by leading experts in the field combine new and established material and enable nonexperts to learn more about the techniques and methods used. This book, the first in the area, provides a comprehensive view of the subject and will be welcomed by mathematicians, scientists and engineers, both in industry and academia.
| Author | : Shinichi Nakajima |
| Publisher | : Cambridge University Press |
| Total Pages | : 561 |
| Release | : 2019-07-11 |
| Genre | : Computers |
| ISBN | : 1316997219 |
Download Variational Bayesian Learning Theory Book in PDF, ePub and Kindle
Variational Bayesian learning is one of the most popular methods in machine learning. Designed for researchers and graduate students in machine learning, this book summarizes recent developments in the non-asymptotic and asymptotic theory of variational Bayesian learning and suggests how this theory can be applied in practice. The authors begin by developing a basic framework with a focus on conjugacy, which enables the reader to derive tractable algorithms. Next, it summarizes non-asymptotic theory, which, although limited in application to bilinear models, precisely describes the behavior of the variational Bayesian solution and reveals its sparsity inducing mechanism. Finally, the text summarizes asymptotic theory, which reveals phase transition phenomena depending on the prior setting, thus providing suggestions on how to set hyperparameters for particular purposes. Detailed derivations allow readers to follow along without prior knowledge of the mathematical techniques specific to Bayesian learning.
| Author | : Kevin P. Murphy |
| Publisher | : MIT Press |
| Total Pages | : 858 |
| Release | : 2022-03-01 |
| Genre | : Computers |
| ISBN | : 0262369303 |
Download Probabilistic Machine Learning Book in PDF, ePub and Kindle
A detailed and up-to-date introduction to machine learning, presented through the unifying lens of probabilistic modeling and Bayesian decision theory. This book offers a detailed and up-to-date introduction to machine learning (including deep learning) through the unifying lens of probabilistic modeling and Bayesian decision theory. The book covers mathematical background (including linear algebra and optimization), basic supervised learning (including linear and logistic regression and deep neural networks), as well as more advanced topics (including transfer learning and unsupervised learning). End-of-chapter exercises allow students to apply what they have learned, and an appendix covers notation. Probabilistic Machine Learning grew out of the author’s 2012 book, Machine Learning: A Probabilistic Perspective. More than just a simple update, this is a completely new book that reflects the dramatic developments in the field since 2012, most notably deep learning. In addition, the new book is accompanied by online Python code, using libraries such as scikit-learn, JAX, PyTorch, and Tensorflow, which can be used to reproduce nearly all the figures; this code can be run inside a web browser using cloud-based notebooks, and provides a practical complement to the theoretical topics discussed in the book. This introductory text will be followed by a sequel that covers more advanced topics, taking the same probabilistic approach.
