Principles Of Artificial Neural Networks Basic Designs To Deep Learning 4th Edition PDF Download
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Author | : Graupe Daniel |
Publisher | : World Scientific |
Total Pages | : 440 |
Release | : 2019-03-15 |
Genre | : Computers |
ISBN | : 9811201242 |
Download Principles Of Artificial Neural Networks: Basic Designs To Deep Learning (4th Edition) Book in PDF, ePub and Kindle
The field of Artificial Neural Networks is the fastest growing field in Information Technology and specifically, in Artificial Intelligence and Machine Learning.This must-have compendium presents the theory and case studies of artificial neural networks. The volume, with 4 new chapters, updates the earlier edition by highlighting recent developments in Deep-Learning Neural Networks, which are the recent leading approaches to neural networks. Uniquely, the book also includes case studies of applications of neural networks — demonstrating how such case studies are designed, executed and how their results are obtained.The title 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 | : Daniel Graupe |
Publisher | : |
Total Pages | : 439 |
Release | : 2019 |
Genre | : COMPUTERS |
ISBN | : 9789811201233 |
Download Principles of Artificial Neural Networks Book in PDF, ePub and Kindle
Author | : Daniel Graupe |
Publisher | : World Scientific |
Total Pages | : 320 |
Release | : 2007 |
Genre | : Computers |
ISBN | : 9812706240 |
Download Principles of Artificial Neural Networks Book in PDF, ePub and Kindle
This book should serves as a self-study course for engineers and computer scientist in the industry. The features include major neural network approaches and architectures with theories and detailed case studies for each of the approaches acompanied by complete computer codes and the corresponding computed results. There is also a chapter on LAMSTAR neural network.
Author | : Daniel Graupe |
Publisher | : World Scientific |
Total Pages | : 256 |
Release | : 1997-05-01 |
Genre | : Mathematics |
ISBN | : 9789810241254 |
Download Principles of Artificial Neural Networks Book in PDF, ePub and Kindle
This textbook is intended for a first-year graduate course on Artificial Neural Networks. It assumes no prior background in the subject and is directed to MS students in electrical engineering, computer science and related fields, with background in at least one programming language or in a programming tool such as Matlab, and who have taken the basic undergraduate classes in systems or in signal processing.
Author | : Giuseppe Ciaburro |
Publisher | : Packt Publishing Ltd |
Total Pages | : 270 |
Release | : 2017-09-27 |
Genre | : Computers |
ISBN | : 1788399412 |
Download Neural Networks with R Book in PDF, ePub and Kindle
Uncover the power of artificial neural networks by implementing them through R code. About This Book Develop a strong background in neural networks with R, to implement them in your applications Build smart systems using the power of deep learning Real-world case studies to illustrate the power of neural network models Who This Book Is For This book is intended for anyone who has a statistical background with knowledge in R and wants to work with neural networks to get better results from complex data. If you are interested in artificial intelligence and deep learning and you want to level up, then this book is what you need! What You Will Learn Set up R packages for neural networks and deep learning Understand the core concepts of artificial neural networks Understand neurons, perceptrons, bias, weights, and activation functions Implement supervised and unsupervised machine learning in R for neural networks Predict and classify data automatically using neural networks Evaluate and fine-tune the models you build. In Detail Neural networks are one of the most fascinating machine learning models for solving complex computational problems efficiently. Neural networks are used to solve wide range of problems in different areas of AI and machine learning. This book explains the niche aspects of neural networking and provides you with foundation to get started with advanced topics. The book begins with neural network design using the neural net package, then you'll build a solid foundation knowledge of how a neural network learns from data, and the principles behind it. This book covers various types of neural network including recurrent neural networks and convoluted neural networks. You will not only learn how to train neural networks, but will also explore generalization of these networks. Later we will delve into combining different neural network models and work with the real-world use cases. By the end of this book, you will learn to implement neural network models in your applications with the help of practical examples in the book. Style and approach A step-by-step guide filled with real-world practical examples.
Author | : Robert L. Harvey |
Publisher | : Prentice Hall |
Total Pages | : 197 |
Release | : 1994 |
Genre | : Neural networks (Computer science) |
ISBN | : 9780131121942 |
Download Neural Network Principles Book in PDF, ePub and Kindle
This text presents basic ideas of neural networks (theory, design and principles) in mathematical form - using models of biological systems as springboards to a broad range of applications.
Author | : Daniel Graupe |
Publisher | : World Scientific |
Total Pages | : 382 |
Release | : 2013-07-31 |
Genre | : Computers |
ISBN | : 9814522759 |
Download Principles Of Artificial Neural Networks (3rd Edition) 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 — 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 | : Martin T. Hagan |
Publisher | : |
Total Pages | : |
Release | : 2003 |
Genre | : Neural networks (Computer science) |
ISBN | : 9789812403766 |
Download Neural Network Design Book in PDF, ePub and Kindle
Author | : Jon Adams |
Publisher | : Green Mountain Computing |
Total Pages | : 83 |
Release | : |
Genre | : Computers |
ISBN | : |
Download AI Foundations of Neural Networks Book in PDF, ePub and Kindle
Dive into the fascinating world of artificial intelligence with "AI Foundations of Neural Networks." This comprehensive guide demystifies the complex concepts of neural networks, offering a clear and accessible path to understanding the core principles that fuel modern AI systems. From the basic building blocks of neural networks to advanced architectures, this book is designed to provide a thorough grounding in deep learning for readers at all levels of expertise. Chapters Overview: The Neuron - The Fundamental Unit: Explore the basic structure that mimics the human brain's neurons, setting the stage for understanding how neural networks operate. Activation Functions - Bringing Neurons to Life: Learn about the functions that help neural networks make decisions, allowing them to process information in complex ways. The Anatomy of Layers: Delve into how layers of neurons work together to process data, forming the backbone of neural network architecture. Backpropagation - Learning from Errors: Understand the mechanism by which neural networks learn from their mistakes, optimizing their performance over time. Loss Functions - Measuring Performance: Discover how neural networks evaluate their accuracy and make adjustments to improve their predictions. Optimization Algorithms - The Road to Convergence: Get to grips with the strategies that guide neural networks towards making more accurate predictions. Overfitting and Generalization: Learn about the challenges of making models that perform well not just on the data they were trained on but on new, unseen data as well. Advanced Architectures: Explore the frontier of neural network design, including the latest models that drive progress in AI research. Why This Book? "AI Foundations of Neural Networks" stands out as a beacon of knowledge, transforming what might appear as a complex field into a series of comprehensible concepts. With a focus on clarity, practical insights, and intuitive understanding, this book bridges the gap between theoretical knowledge and real-world application. Whether you're a student, professional, or enthusiast eager to navigate the realm of AI, this guide illuminates the path forward. Embark on a journey through the corridors of deep learning with "AI Foundations of Neural Networks." Unlock the secrets behind the artificial intelligence technologies that are transforming our world. Your exploration of neural networks starts here. Perfect for: Students, AI professionals, tech enthusiasts, and anyone curious about the inner workings of neural networks and deep learning. Discover the principles of AI that are shaping the future. Your journey into neural networks begins now.
Author | : Philip Johannes Gouverneur |
Publisher | : Logos Verlag Berlin GmbH |
Total Pages | : 228 |
Release | : 2024-06-14 |
Genre | : Mathematics |
ISBN | : 3832582576 |
Download Machine Learning Methods for Pain Investigation Using Physiological Signals Book in PDF, ePub and Kindle
Pain assessment has remained largely unchanged for decades and is currently based on self-reporting. Although there are different versions, these self-reports all have significant drawbacks. For example, they are based solely on the individual’s assessment and are therefore influenced by personal experience and highly subjective, leading to uncertainty in ratings and difficulty in comparability. Thus, medicine could benefit from an automated, continuous and objective measure of pain. One solution is to use automated pain recognition in the form of machine learning. The aim is to train learning algorithms on sensory data so that they can later provide a pain rating. This thesis summarises several approaches to improve the current state of pain recognition systems based on physiological sensor data. First, a novel pain database is introduced that evaluates the use of subjective and objective pain labels in addition to wearable sensor data for the given task. Furthermore, different feature engineering and feature learning approaches are compared using a fair framework to identify the best methods. Finally, different techniques to increase the interpretability of the models are presented. The results show that classical hand-crafted features can compete with and outperform deep neural networks. Furthermore, the underlying features are easily retrieved from electrodermal activity for automated pain recognition, where pain is often associated with an increase in skin conductance.