Containing contributions from some of the world's leading specialists in their fields (including Dr. Ton Coolen and Professor Igor Aleksander), this volume will provide the reader with a good, general introduction to the basic concepts ...
Author: Lawrence J. Landau
Publisher: Springer Science & Business Media
Concepts for Neural Networks - A Survey provides a wide-ranging survey of concepts relating to the study of neural networks. It includes chapters explaining the basics of both artificial neural networks and the mathematics of neural networks, as well as chapters covering the more philosophical background to the topic and consciousness. There is also significant emphasis on the practical use of the techniques described in the area of robotics. Containing contributions from some of the world's leading specialists in their fields (including Dr. Ton Coolen and Professor Igor Aleksander), this volume will provide the reader with a good, general introduction to the basic concepts needed to understan d and use neural network technology.
Release on 2019-10-11 | by Management Association, Information Resources
Highlighting a range of topics such as natural language processing, predictive analytics, and deep neural networks, this multi-volume book is ideally designed for computer engineers, software developers, IT professionals, academicians, ...
Author: Management Association, Information Resources
Publisher: IGI Global
Due to the growing use of web applications and communication devices, the use of data has increased throughout various industries. It is necessary to develop new techniques for managing data in order to ensure adequate usage. Deep learning, a subset of artificial intelligence and machine learning, has been recognized in various real-world applications such as computer vision, image processing, and pattern recognition. The deep learning approach has opened new opportunities that can make such real-life applications and tasks easier and more efficient. Deep Learning and Neural Networks: Concepts, Methodologies, Tools, and Applications is a vital reference source that trends in data analytics and potential technologies that will facilitate insight in various domains of science, industry, business, and consumer applications. It also explores the latest concepts, algorithms, and techniques of deep learning and data mining and analysis. Highlighting a range of topics such as natural language processing, predictive analytics, and deep neural networks, this multi-volume book is ideally designed for computer engineers, software developers, IT professionals, academicians, researchers, and upper-level students seeking current research on the latest trends in the field of deep learning.
Neural Networks presents concepts of neural-network models and techniques of parallel distributed processing in a three-step approach: - A brief overview of the neural structure of the brain and the history of neural-network modeling ...
Author: Berndt Müller
Publisher: Springer Science & Business Media
Neural Networks presents concepts of neural-network models and techniques of parallel distributed processing in a three-step approach: - A brief overview of the neural structure of the brain and the history of neural-network modeling introduces to associative memory, preceptrons, feature-sensitive networks, learning strategies, and practical applications. - The second part covers subjects like statistical physics of spin glasses, the mean-field theory of the Hopfield model, and the "space of interactions" approach to the storage capacity of neural networks. - The final part discusses nine programs with practical demonstrations of neural-network models. The software and source code in C are on a 3 1/2" MS-DOS diskette can be run with Microsoft, Borland, Turbo-C, or compatible compilers.
Pedagogically sound and clearly written, this text discusses: Neuronal morphology and neuro-computational systems Threshold logic, adaptation, and learning Static neural networks–MFNNs, XOR Neural Networks, and Backpropagation Algorithms ...
Author: Madan Gupta
Publisher: John Wiley & Sons
Provides comprehensive treatment of the theory of both static and dynamic neural networks. * Theoretical concepts are illustrated by reference to practical examples Includes end-of-chapter exercises and end-of-chapter exercises. *An Instructor Support FTP site is available from the Wiley editorial department.
This self-contained guide will benefit those who seek to both understand the theory behind deep learning, and to gain hands-on experience in implementing ConvNets in practice.
Author: Hamed Habibi Aghdam
This must-read text/reference introduces the fundamental concepts of convolutional neural networks (ConvNets), offering practical guidance on using libraries to implement ConvNets in applications of traffic sign detection and classification. The work presents techniques for optimizing the computational efficiency of ConvNets, as well as visualization techniques to better understand the underlying processes. The proposed models are also thoroughly evaluated from different perspectives, using exploratory and quantitative analysis. Topics and features: explains the fundamental concepts behind training linear classifiers and feature learning; discusses the wide range of loss functions for training binary and multi-class classifiers; illustrates how to derive ConvNets from fully connected neural networks, and reviews different techniques for evaluating neural networks; presents a practical library for implementing ConvNets, explaining how to use a Python interface for the library to create and assess neural networks; describes two real-world examples of the detection and classification of traffic signs using deep learning methods; examines a range of varied techniques for visualizing neural networks, using a Python interface; provides self-study exercises at the end of each chapter, in addition to a helpful glossary, with relevant Python scripts supplied at an associated website. This self-contained guide will benefit those who seek to both understand the theory behind deep learning, and to gain hands-on experience in implementing ConvNets in practice. As no prior background knowledge in the field is required to follow the material, the book is ideal for all students of computer vision and machine learning, and will also be of great interest to practitioners working on autonomous cars and advanced driver assistance systems.
As book review editor of the IEEE Transactions on Neural Networks, Mohamad Hassoun has had the opportunity to assess the multitude of books on artificial neural networks that have appeared in recent years.
Author: Mohamad H. Hassoun
Publisher: MIT Press
As book review editor of the IEEE Transactions on Neural Networks, Mohamad Hassoun has had the opportunity to assess the multitude of books on artificial neural networks that have appeared in recent years. Now, in Fundamentals of Artificial Neural Networks, he provides the first systematic account of artificial neural network paradigms by identifying clearly the fundamental concepts and major methodologies underlying most of the current theory and practice employed by neural network researchers. Such a systematic and unified treatment, although sadly lacking in most recent texts on neural networks, makes the subject more accessible to students and practitioners. Here, important results are integrated in order to more fully explain a wide range of existing empirical observations and commonly used heuristics. There are numerous illustrative examples, over 200 end-of-chapter analytical and computer-based problems that will aid in the development of neural network analysis and design skills, and a bibliography of nearly 700 references. Proceeding in a clear and logical fashion, the first two chapters present the basic building blocks and concepts of artificial neural networks and analyze the computational capabilities of the basic network architectures involved. Supervised, reinforcement, and unsupervised learning rules in simple nets are brought together in a common framework in chapter three. The convergence and solution properties of these learning rules are then treated mathematically in chapter four, using the "average learning equation" analysis approach. This organization of material makes it natural to switch into learning multilayer nets using backprop and its variants, described in chapter five. Chapter six covers most of the major neural network paradigms, while associative memories and energy minimizing nets are given detailed coverage in the next chapter. The final chapter takes up Boltzmann machines and Boltzmann learning along with other global search/optimization algorithms such as stochastic gradient search, simulated annealing, and genetic algorithms.
In this book you will start by exploring back propagation and unsupervised neural networks with Unity and C#. You’ll then move onto activation functions, such as sigmoid functions, step functions, and so on.
Author: Abhishek Nandy
Learn the core concepts of neural networks and discover the different types of neural network, using Unity as your platform. In this book you will start by exploring back propagation and unsupervised neural networks with Unity and C#. You’ll then move onto activation functions, such as sigmoid functions, step functions, and so on. The author also explains all the variations of neural networks such as feed forward, recurrent, and radial. Once you’ve gained the basics, you’ll start programming Unity with C#. In this section the author discusses constructing neural networks for unsupervised learning, representing a neural network in terms of data structures in C#, and replicating a neural network in Unity as a simulation. Finally, you’ll define back propagation with Unity C#, before compiling your project. What You'll Learn Discover the concepts behind neural networks Work with Unity and C# See the difference between fully connected and convolutional neural networks Master neural network processing for Windows 10 UWP Who This Book Is For Gaming professionals, machine learning and deep learning enthusiasts.
The book's main aim is to cover the basic concepts, with the key neural network models explored sufficiently deeply to allow a competent programmer to implement the networks in a language of their choice.
Author: Robert Callan
Publisher: Prentice Hall
The aim of this work is to cover the basic concepts, with the key neural network models explored sufficiently deeply to allow a competent programmer to implement the networks in a language of their choice. The book is supported by a website.
You should now be able to link some of the concepts in Machine Learning with the work you do, or the work you want to do. Once you practice the models in the book, you can build your very own models in either R or Python.
Author: Peter Bradley
Publisher: Peter Bradley
Do you know how to build a Machine Learning Algorithm in Python? Have you learned how to build a Neural Network in Python? If you have read the first three books in the series, you will know how to do both those things. If you want to learn more about the concepts related to Machine Learning, and some subjects and concepts that are linked to Machine Learning, you have come to the right place. Over the course of the book, you will gather information on the following: Subjects linked to Machine Learning Artificial Intelligence Big Data Building Generic Algorithms in Python Activation functions used to build Neural Networks Building a Neural Network in R The information in this book will help you learn more about Machine Learning. You should now be able to link some of the concepts in Machine Learning with the work you do, or the work you want to do. Once you practice the models in the book, you can build your very own models in either R or Python. So What are You Waiting For? It is never to early or late to learn. Grab a copy of this book Now, and build your very own genetic Algorithm in Python and a Neural Network in R.
This book covers both classical and modern models in deep learning.
Author: Charu C. Aggarwal
This book covers both classical and modern models in deep learning. The primary focus is on the theory and algorithms of deep learning. The theory and algorithms of neural networks are particularly important for understanding important concepts, so that one can understand the important design concepts of neural architectures in different applications. Why do neural networks work? When do they work better than off-the-shelf machine-learning models? When is depth useful? Why is training neural networks so hard? What are the pitfalls? The book is also rich in discussing different applications in order to give the practitioner a flavor of how neural architectures are designed for different types of problems. Applications associated with many different areas like recommender systems, machine translation, image captioning, image classification, reinforcement-learning based gaming, and text analytics are covered. The chapters of this book span three categories: The basics of neural networks: Many traditional machine learning models can be understood as special cases of neural networks. An emphasis is placed in the first two chapters on understanding the relationship between traditional machine learning and neural networks. Support vector machines, linear/logistic regression, singular value decomposition, matrix factorization, and recommender systems are shown to be special cases of neural networks. These methods are studied together with recent feature engineering methods like word2vec. Fundamentals of neural networks: A detailed discussion of training and regularization is provided in Chapters 3 and 4. Chapters 5 and 6 present radial-basis function (RBF) networks and restricted Boltzmann machines. Advanced topics in neural networks: Chapters 7 and 8 discuss recurrent neural networks and convolutional neural networks. Several advanced topics like deep reinforcement learning, neural Turing machines, Kohonen self-organizing maps, and generative adversarial networks are introduced in Chapters 9 and 10. The book is written for graduate students, researchers, and practitioners. Numerous exercises are available along with a solution manual to aid in classroom teaching. Where possible, an application-centric view is highlighted in order to provide an understanding of the practical uses of each class of techniques.
This work describes a mathematical concept of modelling field theory and its applications to a variety of problems, while offering a view of the relationships among mathematics, computational concepts in neural networks, semiotics, and ...
Author: Leonid I. Perlovsky
This work describes a mathematical concept of modelling field theory and its applications to a variety of problems, while offering a view of the relationships among mathematics, computational concepts in neural networks, semiotics, and concepts of mind in psychology and philosophy.
Master the World of Machine Learning - Even if You're a Complete Beginner.Are you an aspiring entrepreneur? Are you an amateur software developer looking for a break in the world of machine learning? Then this is the book for you.
This book is an ideal place to start your journey as • Core concepts of machine learning algorithms are explained in plain English using illustrations, data tables and examples • Intuitive meaning of the mathematics behind popular ...
Author: Suresh Samudrala
Publisher: Notion Press
Artificial intelligence and machine learning are considered as hot technologies of this century. As these technologies move from research labs to enterprise data centers, the need for skilled professionals is continuously on the rise. This book is intended for IT and business professionals looking to gain proficiency in these technologies but are turned off by the complex mathematical equations. This book is also useful for students in the area of artificial intelligence and machine learning to gain a conceptual understanding of the algorithms and get an industry perspective. This book is an ideal place to start your journey as • Core concepts of machine learning algorithms are explained in plain English using illustrations, data tables and examples • Intuitive meaning of the mathematics behind popular machine learning algorithms explained • Covers classical machine learning, neural networks and deep learning algorithms At a time when the IT industry is focusing on reskilling its vast human resources, Machine intelligence is a very timely publication. It has a simple approach that builds up from basics, which would help software engineers and students looking to learn about the field as well as those who might have started off without the benefit of a structured introduction or sound basics. Highly recommended. - Siddhartha S, Founder and CEO of Intain - Financial technology startup Suresh has written a very accessible book for practitioners. The book has depth yet avoids excessive mathematics. The coverage of the subject is very good and has most of the concepts required for understanding machine learning if someone is looking for depth. For senior management, it will provide a good overview. It is well written. I highly recommend it. - Whee Teck ONG, CEO of Trusted Source and VP of Singapore Computer Society
Release on 2011-07-31 | by Management Association, Information Resources
"This reference offers a wide-ranging selection of key research in a complex field of study,discussing topics ranging from using machine learning to improve the effectiveness of agents and multi-agent systems to developing machine learning ...
Author: Management Association, Information Resources
Publisher: IGI Global
"This reference offers a wide-ranging selection of key research in a complex field of study,discussing topics ranging from using machine learning to improve the effectiveness of agents and multi-agent systems to developing machine learning software for high frequency trading in financial markets"--Provided by publishe
The object of this book is to answer all of these questions. This book will give an initiation to machine learning methods. In fact, this book will give the very fundamental concepts of machine learning methods with no pre-requisite skills.
Author: Daniel Géron
What is machine learning? How machine learning works? Should I use a machine learning model or another approach to solve my problem? How do I implement machine learning to my problem? What are the machine learning methods I can use for my problem? How do I know my machine learning model is efficient? Are you wondering all these questions and hesitate on how to start with machine learning? The object of this book is to answer all of these questions. This book will give an initiation to machine learning methods. In fact, this book will give the very fundamental concepts of machine learning methods with no pre-requisite skills. Machine learning include is a large domain of research and have different branches. This book will teach the concepts of machine learning in general and also how to use artificial neural networks. By acquiring the skills presented in this book, we will be able to decide if machine learning is suited to solve your problem. You will also be able to make a judgement on the best way to implement a machine learning model to solve the problem you have in hand. By reading this book you will learn: - The general concept of machine learning - When to use and when to avoid machine learning - The 4 main types of machine learning - When to use each type of machine learning - The general concept of artificial neural networks - Activation function in artificial neural network and to choose an activation function within an artificial neural network - The 5 main types of artificial neural network - The best function to be used to train artificial neural networks. - the 2 main concepts to know in the training process of the artificial neural network - the main variants and algorithms for the formation of an artificial neural network and a machine learning model in general. Even you don't have any mathematical background or a statistical skill, this book will help you develop a sound understanding of machine learning methods and artificial neural networks.
Are you thinking of learning more about Artificial Neural Network? This book has been written in layman's terms as an introduction to neural networks and their algorithms. Each algorithm is explained very easily for more understanding.
***** Buy now (Will soon return to $75.99 + Special Offer Below) ***** Free Kindle eBook for customers who purchase the print book from Amazon Are you thinking of learning more about Artificial Neural Network? This book has been written in layman's terms as an introduction to neural networks and their algorithms. Each algorithm is explained very easily for more understanding. Several Visual Illustrations and Examples Instead of tough math formulas, this book contains several graphs and images which detail all algorithms and their applications in all area of the real life. Why this book is different ? An Artificial Neural Network (ANN) is a computational model. It is based on the structure and functions of biological neural networks. It works like the way human (animal) brain processes information. It includes a large number of connected processing units called neurons that work together to process information. They also generate meaningful results from it. In this book, we will take you through the complete introduction to Artificial Neural Network, Artificial Neural Network Structure, layers of ANN, Applications, Algorithms, Tools and technology, Practical implementations and the benefits and limitations of ANN. This book takes a different approach that is based on providing simple examples of how ANN algorithms work, and building on those examples step by step to encompass the more complicated parts of the algorithms. Target Users The book designed for a variety of target audiences. The most suitable users would include: Beginners who want to approach ANN, but are too afraid of complex math to start Newbies in computer science techniques and ANN Professionals in data science and social sciences Professors, lecturers or tutors who are looking to find better ways to explain the content to their students in the simplest and easiest way Students and academicians, especially those focusing on neural networks and deep learning What's inside this book? What is Artificial Neural Network? Why Neural Networks? Major Variants of Artificial Neural Network Tools and Technologies Practical implementations Major NN projects Open sources resources Issues and Challenges Applications of ANN Deep Learning: What & Why? Our Future with Deep Learning Applied The Long-Term Vision of Deep Learning Glossary of Some Useful Terms in Neural Networks Frequently Asked Questions Q: Is this book for me and do I need programming experience? A: If you want to learn more about deep learning with practical applications, this book is for you. This book has been written in layman's terms as an introduction to neural networks and their algorithms. Each algorithm is explained very easily for more understanding. No coding experience is required. Some practical examples is presented with Python but it is not the major part of the book. Q: Can I loan this book to friends? A: Yes. Under Amazon's Kindle Book Lending program, you can lend this book to friends and family for a duration of 14 days. Q: Does this book include everything I need to become a Neural Networks expert? A: Unfortunately, no. This book is designed for readers taking their first steps in neural networks and further learning will be required beyond this book to master all aspects of neural networks. Q: Can I have a refund if this book is not fitted for me? A: Yes, Amazon refund you if you aren't satisfied, for more information about the amazon refund service please go to the amazon help platform. will also be happy to help you if you send us an email at [email protected]