Manual for the implementation of neural networks in MATLAB
Title | Manual for the implementation of neural networks in MATLAB PDF eBook |
Author | Michael Kuhn |
Publisher | GRIN Verlag |
Pages | 45 |
Release | 2005-12-05 |
Genre | Business & Economics |
ISBN | 3638445518 |
Bachelor Thesis from the year 2005 in the subject Business economics - Information Management, grade: 2,0, Neisse University Görlitz (Neisse University), language: English, abstract: This bachelor thesis presents a manual about the implementation of neural networks in the software environment MATLAB. The thesis can be divided into four parts. After an introduction into the thesis, the theoretical background of neural networks and MATLAB is explained in two chapters. The third part is the description how to implement networks in a general way and with examples, too. The manual is created for the “Master Course of Computer Studies” at the University of Applied Science Zittau/Görlitz. Due to the fact, that this manual is a bachelor thesis just a small theoretical and practical overview about neural networks can be given.
MATLAB Neural Network Toolbox
Title | MATLAB Neural Network Toolbox PDF eBook |
Author | Howard Demuth |
Publisher | |
Pages | |
Release | 1997 |
Genre | |
ISBN |
Neural Network Design
Title | Neural Network Design PDF eBook |
Author | Martin T. Hagan |
Publisher | |
Pages | |
Release | 2003 |
Genre | Neural networks (Computer science) |
ISBN | 9789812403766 |
MATLAB Deep Learning
Title | MATLAB Deep Learning PDF eBook |
Author | Phil Kim |
Publisher | Apress |
Pages | 162 |
Release | 2017-06-15 |
Genre | Computers |
ISBN | 1484228456 |
Get started with MATLAB for deep learning and AI with this in-depth primer. In this book, you start with machine learning fundamentals, then move on to neural networks, deep learning, and then convolutional neural networks. In a blend of fundamentals and applications, MATLAB Deep Learning employs MATLAB as the underlying programming language and tool for the examples and case studies in this book. With this book, you'll be able to tackle some of today's real world big data, smart bots, and other complex data problems. You’ll see how deep learning is a complex and more intelligent aspect of machine learning for modern smart data analysis and usage. What You'll Learn Use MATLAB for deep learning Discover neural networks and multi-layer neural networks Work with convolution and pooling layers Build a MNIST example with these layers Who This Book Is For Those who want to learn deep learning using MATLAB. Some MATLAB experience may be useful.
ADVANCED TOPICS IN NEURAL NETWORKS WITH MATLAB. PARALLEL COMPUTING, OPTIMIZE AND TRAINING
Title | ADVANCED TOPICS IN NEURAL NETWORKS WITH MATLAB. PARALLEL COMPUTING, OPTIMIZE AND TRAINING PDF eBook |
Author | PEREZ C. |
Publisher | CESAR PEREZ |
Pages | 78 |
Release | 2023-12-13 |
Genre | Computers |
ISBN | 1974082040 |
Neural networks are inherently parallel algorithms. Multicore CPUs, graphical processing units (GPUs), and clusters of computers with multiple CPUs and GPUs can take advantage of this parallelism. Parallel Computing Toolbox, when used in conjunction with Neural Network Toolbox, enables neural network training and simulation to take advantage of each mode of parallelism. Parallel Computing Toolbox allows neural network training and simulation to run across multiple CPU cores on a single PC, or across multiple CPUs on multiple computers on a network using MATLAB Distributed Computing Server. Using multiple cores can speed calculations. Using multiple computers can allow you to solve problems using data sets too big to fit in the RAM of a single computer. The only limit to problem size is the total quantity of RAM available across all computers. Distributed and GPU computing can be combined to run calculations across multiple CPUs and/or GPUs on a single computer, or on a cluster with MATLAB Distributed Computing Server. It is desirable to determine the optimal regularization parameters in an automated fashion. One approach to this process is the Bayesian framework. In this framework, the weights and biases of the network are assumed to be random variables with specified distributions. The regularization parameters are related to the unknown variances associated with these distributions. You can then estimate these parameters using statistical techniques. It is very difficult to know which training algorithm will be the fastest for a given problem. It depends on many factors, including the complexity of the problem, the number of data points in the training set, the number of weights and biases in the network, the error goal, and whether the network is being used for pattern recognition (discriminant analysis) or function approximation (regression). This book compares the various training algorithms. One of the problems that occur during neural network training is called overfitting. The error on the training set is driven to a very small value, but when new data is presented to the network the error is large. The network has memorized the training examples, but it has not learned to generalize to new situations. This book develops the following topics: Neural Networks with Parallel and GPU Computing Deep Learning Optimize Neural Network Training Speed and Memory Improve Neural Network Generalization and Avoid Overfitting Create and Train Custom Neural Network Architectures Deploy Training of Neural Networks Perceptron Neural Networks Linear Neural Networks Hopfield Neural Network Neural Network Object Reference Neural Network Simulink Block Library Deploy Neural Network Simulink Diagrams
Begründete und aktenmäßige Beantwortung der von denen exmittirten Anspanner zu Lobichau, wegen derer schuldigen Mauerstein-Fuhren und Frohnen in der zum Druck 1708 übergebenen sogenannten Deduction angeführten nichtigen und irrigen Vorstellungen
Title | Begründete und aktenmäßige Beantwortung der von denen exmittirten Anspanner zu Lobichau, wegen derer schuldigen Mauerstein-Fuhren und Frohnen in der zum Druck 1708 übergebenen sogenannten Deduction angeführten nichtigen und irrigen Vorstellungen PDF eBook |
Author | |
Publisher | |
Pages | |
Release | 1722 |
Genre | |
ISBN |
Guide to Neural Computing Applications
Title | Guide to Neural Computing Applications PDF eBook |
Author | Lionel Tarassenko |
Publisher | Elsevier |
Pages | 151 |
Release | 1998-01-30 |
Genre | Computers |
ISBN | 0080512607 |
Neural networks have shown enormous potential for commercial exploitation over the last few years but it is easy to overestimate their capabilities. A few simple algorithms will learn relationships between cause and effect or organise large volumes of data into orderly and informative patterns but they cannot solve every problem and consequently their application must be chosen carefully and appropriately. This book outlines how best to make use of neural networks. It enables newcomers to the technology to construct robust and meaningful non-linear models and classifiers and benefits the more experienced practitioner who, through over familiarity, might otherwise be inclined to jump to unwarranted conclusions. The book is an invaluable resource not only for those in industry who are interested in neural computing solutions, but also for final year undergraduates or graduate students who are working on neural computing projects. It provides advice which will help make the best use of the growing number of commercial and public domain neural network software products, freeing the specialist from dependence upon external consultants.