Neural Network Design


Book Description




MATLAB Deep Learning


Book Description

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


Book Description

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




Data Science Quick Reference Manual – Deep Learning


Book Description

This work follows the 2021 curriculum of the Association for Computing Machinery for specialists in Data Sciences, with the aim of producing a manual that collects notions in a simplified form, facilitating a personal training path starting from specialized skills in Computer Science or Mathematics or Statistics. It has a bibliography with links to quality material but freely usable for your own training and contextual practical exercises. Part in a series of texts, it first summarizes the standard CRISP DM working methodology used in this work and in Data Science projects. As this text uses Orange for the application aspects, it describes its installation and widgets. The data modeling phase is considered from the perspective of machine learning by summarizing machine learning types, model types, problem types, and algorithm types. Deep Learning techniques are described considering the architectures of the Perceptron, Neocognitron, the neuron with Backpropagation and the activation functions, the Feed Forward Networks, the Autoencoders, the recurrent networks and the LSTM and GRU, the Transformer Neural Networks, the Convolutional Neural Networks and Generative Adversarial Networks and analyzed the building blocks. Regularization techniques (Dropout, Early stopping and others), visual design and simulation techniques and tools, the most used algorithms and the best known architectures (LeNet, VGGnet, ResNet, Inception and others) are considered, closing with a set of practical tips and tricks. The exercises are described with Orange and Python using the Keras/Tensorflow library. The text is accompanied by supporting material and it is possible to download the examples and the test data.




Computational Statistics Handbook with MATLAB


Book Description

A Strong Practical Focus on Applications and AlgorithmsComputational Statistics Handbook with MATLAB, Third Edition covers today's most commonly used techniques in computational statistics while maintaining the same philosophy and writing style of the bestselling previous editions. The text keeps theoretical concepts to a minimum, emphasizing the i




Proceedings of the 2011 International Conference on Informatics, Cybernetics, and Computer Engineering (ICCE2011) November 19-20, 2011, Melbourne, Australia


Book Description

The volume includes a set of selected papers extended and revised from the International Conference on Informatics, Cybernetics, and Computer Engineering. A computer network, often simply referred to as a network, is a collection of computers and devices interconnected by communications channels that facilitate communications and allows sharing of resources and information among interconnected devices. Put more simply, a computer network is a collection of two or more computers linked together for the purposes of sharing information, resources, among other things. Computer networking or Data Communications (Datacom) is the engineering discipline concerned with computer networks. Computer networking is sometimes considered a sub-discipline of electrical engineering, telecommunications, computer science, information technology and/or computer engineering since it relies heavily upon the theoretical and practical application of these scientific and engineering disciplines. Networks may be classified according to a wide variety of characteristics such as medium used to transport the data, communications protocol used, scale, topology, organizational scope, etc. Electronics engineering, also referred to as electronic engineering, is an engineering discipline where non-linear and active electrical components such as electron tubes, and semiconductor devices, especially transistors, diodes and integrated circuits, are utilized to design electronic circuits, devices and systems, typically also including passive electrical components and based on printed circuit boards. The term denotes a broad engineering field that covers important subfields such as analog electronics, digital electronics, consumer electronics, embedded systems and power electronics. Electronics engineering deals with implementation of applications, principles and algorithms developed within many related fields, for example solid-state physics, radio engineering, telecommunications, control systems, signal processing, systems engineering, computer engineering, instrumentation engineering, electric power control, robotics, and many others. ICCE 2011 Volume 3 is to provide a forum for researchers, educators, engineers, and government officials involved in the general areas of Computer Engineering and Electronic Engineering to disseminate their latest research results and exchange views on the future research directions of these fields. 99 high-quality papers are included in the volume. Each paper has been peer-reviewed by at least 2 program committee members and selected by the volume editor. Special thanks to editors, staff of association and every participants of the conference. It’s you make the conference a success. We look forward to meeting you next year.




Neural Networks in the Analysis and Design of Structures


Book Description

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.




The Perceptron


Book Description




Artificial Neural Networks and Machine Learning - ICANN 2011


Book Description

This two volume set (LNCS 6791 and LNCS 6792) constitutes the refereed proceedings of the 21th International Conference on Artificial Neural Networks, ICANN 2011, held in Espoo, Finland, in June 2011. The 106 revised full or poster papers presented were carefully reviewed and selected from numerous submissions. ICANN 2011 had two basic tracks: brain-inspired computing and machine learning research, with strong cross-disciplinary interactions and applications.




FPGA Implementations of Neural Networks


Book Description

During the 1980s and early 1990s there was signi?cant work in the design and implementation of hardware neurocomputers. Nevertheless, most of these efforts may be judged to have been unsuccessful: at no time have have ha- ware neurocomputers been in wide use. This lack of success may be largely attributed to the fact that earlier work was almost entirely aimed at developing custom neurocomputers, based on ASIC technology, but for such niche - eas this technology was never suf?ciently developed or competitive enough to justify large-scale adoption. On the other hand, gate-arrays of the period m- tioned were never large enough nor fast enough for serious arti?cial-neur- network (ANN) applications. But technology has now improved: the capacity and performance of current FPGAs are such that they present a much more realistic alternative. Consequently neurocomputers based on FPGAs are now a much more practical proposition than they have been in the past. This book summarizes some work towards this goal and consists of 12 papers that were selected, after review, from a number of submissions. The book is nominally divided into three parts: Chapters 1 through 4 deal with foundational issues; Chapters 5 through 11 deal with a variety of implementations; and Chapter 12 looks at the lessons learned from a large-scale project and also reconsiders design issues in light of current and future technology.