Fundamentals Of Artificial Neural Networks PDF Download
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Author | : Mohamad H. Hassoun |
Publisher | : MIT Press |
Total Pages | : 546 |
Release | : 1995 |
Genre | : Computers |
ISBN | : 9780262082396 |
Download Fundamentals of Artificial Neural Networks Book in PDF, ePub and Kindle
A systematic account of artificial neural network paradigms that identifies fundamental concepts and major methodologies. Important results are integrated into the text in order to explain a wide range of existing empirical observations and commonly used heuristics.
Author | : HASSOUN MOHAMAD H |
Publisher | : |
Total Pages | : 540 |
Release | : 1999 |
Genre | : |
ISBN | : 9788120313569 |
Download Fundamentals Of Artificial Neural Networks Book in PDF, ePub and Kindle
Author | : Osval Antonio Montesinos López |
Publisher | : Springer Nature |
Total Pages | : 707 |
Release | : 2022-02-14 |
Genre | : Technology & Engineering |
ISBN | : 3030890104 |
Download Multivariate Statistical Machine Learning Methods for Genomic Prediction Book in PDF, ePub and Kindle
This book is open access under a CC BY 4.0 license This open access book brings together the latest genome base prediction models currently being used by statisticians, breeders and data scientists. It provides an accessible way to understand the theory behind each statistical learning tool, the required pre-processing, the basics of model building, how to train statistical learning methods, the basic R scripts needed to implement each statistical learning tool, and the output of each tool. To do so, for each tool the book provides background theory, some elements of the R statistical software for its implementation, the conceptual underpinnings, and at least two illustrative examples with data from real-world genomic selection experiments. Lastly, worked-out examples help readers check their own comprehension.The book will greatly appeal to readers in plant (and animal) breeding, geneticists and statisticians, as it provides in a very accessible way the necessary theory, the appropriate R code, and illustrative examples for a complete understanding of each statistical learning tool. In addition, it weighs the advantages and disadvantages of each tool.
Author | : Kishan Mehrotra |
Publisher | : MIT Press |
Total Pages | : 376 |
Release | : 1997 |
Genre | : Computers |
ISBN | : 9780262133289 |
Download Elements of Artificial Neural Networks Book in PDF, ePub and Kindle
Elements of Artificial Neural Networks provides a clearly organized general introduction, focusing on a broad range of algorithms, for students and others who want to use neural networks rather than simply study them. The authors, who have been developing and team teaching the material in a one-semester course over the past six years, describe most of the basic neural network models (with several detailed solved examples) and discuss the rationale and advantages of the models, as well as their limitations. The approach is practical and open-minded and requires very little mathematical or technical background. Written from a computer science and statistics point of view, the text stresses links to contiguous fields and can easily serve as a first course for students in economics and management. The opening chapter sets the stage, presenting the basic concepts in a clear and objective way and tackling important -- yet rarely addressed -- questions related to the use of neural networks in practical situations. Subsequent chapters on supervised learning (single layer and multilayer networks), unsupervised learning, and associative models are structured around classes of problems to which networks can be applied. Applications are discussed along with the algorithms. A separate chapter takes up optimization methods. The most frequently used algorithms, such as backpropagation, are introduced early on, right after perceptrons, so that these can form the basis for initiating course projects. Algorithms published as late as 1995 are also included. All of the algorithms are presented using block-structured pseudo-code, and exercises are provided throughout. Software implementing many commonly used neural network algorithms is available at the book's website. Transparency masters, including abbreviated text and figures for the entire book, are available for instructors using the text.
Author | : Fausett |
Publisher | : Prentice Hall |
Total Pages | : 300 |
Release | : 1994 |
Genre | : |
ISBN | : 9780133367690 |
Download Fundamentals of Neural Networks Book in PDF, ePub and Kindle
Author | : Daniel Graupe |
Publisher | : World Scientific |
Total Pages | : 382 |
Release | : 2013 |
Genre | : COMPUTERS |
ISBN | : 9814522740 |
Download Principles of Artificial Neural Networks Book in PDF, ePub and Kindle
Artificial neural networks are most suitable for solving problems that are complex, ill-defined, highly nonlinear, of many and different variables, and/or stochastic. Such problems are abundant in medicine, in finance, in security and beyond. This volume covers the basic theory and architecture of the major artificial neural networks. Uniquely, it presents 18 complete case studies of applications of neural networks in various fields, ranging from cell-shape classification to micro-trading in finance and to constellation recognition OCo all with their respective source codes. These case studies demonstrate to the readers in detail how such case studies are designed and executed and how their specific results are obtained. The book is written for a one-semester graduate or senior-level undergraduate course on artificial neural networks. It is also intended to be a self-study and a reference text for scientists, engineers and for researchers in medicine, finance and data mining."
Author | : Zenon Waszczysznk |
Publisher | : Springer |
Total Pages | : 313 |
Release | : 2014-05-04 |
Genre | : Computers |
ISBN | : 3709124840 |
Download Neural Networks in the Analysis and Design of Structures Book in PDF, ePub and Kindle
Neural Networks are a new, interdisciplinary tool for information processing. Neurocomputing being successfully introduced to structural problems which are difficult or even impossible to be analysed by standard computers (hard computing). The book is devoted to foundations and applications of NNs in the structural mechanics and design of structures.
Author | : Joao Luis Garcia Rosa |
Publisher | : BoD – Books on Demand |
Total Pages | : 416 |
Release | : 2016-10-19 |
Genre | : Computers |
ISBN | : 9535127047 |
Download Artificial Neural Networks Book in PDF, ePub and Kindle
The idea of simulating the brain was the goal of many pioneering works in Artificial Intelligence. The brain has been seen as a neural network, or a set of nodes, or neurons, connected by communication lines. Currently, there has been increasing interest in the use of neural network models. This book contains chapters on basic concepts of artificial neural networks, recent connectionist architectures and several successful applications in various fields of knowledge, from assisted speech therapy to remote sensing of hydrological parameters, from fabric defect classification to application in civil engineering. This is a current book on Artificial Neural Networks and Applications, bringing recent advances in the area to the reader interested in this always-evolving machine learning technique.
Author | : Nikhil Buduma |
Publisher | : "O'Reilly Media, Inc." |
Total Pages | : 365 |
Release | : 2017-05-25 |
Genre | : Computers |
ISBN | : 1491925566 |
Download Fundamentals of Deep Learning Book in PDF, ePub and Kindle
With the reinvigoration of neural networks in the 2000s, deep learning has become an extremely active area of research, one that’s paving the way for modern machine learning. In this practical book, author Nikhil Buduma provides examples and clear explanations to guide you through major concepts of this complicated field. Companies such as Google, Microsoft, and Facebook are actively growing in-house deep-learning teams. For the rest of us, however, deep learning is still a pretty complex and difficult subject to grasp. If you’re familiar with Python, and have a background in calculus, along with a basic understanding of machine learning, this book will get you started. Examine the foundations of machine learning and neural networks Learn how to train feed-forward neural networks Use TensorFlow to implement your first neural network Manage problems that arise as you begin to make networks deeper Build neural networks that analyze complex images Perform effective dimensionality reduction using autoencoders Dive deep into sequence analysis to examine language Learn the fundamentals of reinforcement learning
Author | : Sandhya Samarasinghe |
Publisher | : CRC Press |
Total Pages | : 596 |
Release | : 2016-04-19 |
Genre | : Computers |
ISBN | : 1420013068 |
Download Neural Networks for Applied Sciences and Engineering Book in PDF, ePub and Kindle
In response to the exponentially increasing need to analyze vast amounts of data, Neural Networks for Applied Sciences and Engineering: From Fundamentals to Complex Pattern Recognition provides scientists with a simple but systematic introduction to neural networks. Beginning with an introductory discussion on the role of neural networks in