3rd International Winter School And Conference On Network Science: Netsci-x 2017

Download 3rd International Winter School And Conference On Network Science: Netsci-x 2017

This book contains original research chapters related to the interdisciplinary field of complex networks spanning biological and environmental networks, social, technological, and economic networks.

Many natural phenomena can be modeled as networks where nodes are the primitive compounds and links represent their interactions, similarities, or distances of sorts. Complex networks have an enormous impact on research in various fields like biology, social sciences, engineering, and cyber-security to name a few. The topology of a network often encompasses important information on the functionality and dynamics of the system or the phenomenon it represents. Network science is an emerging interdisciplinary discipline that provides tools and insights to researchers in a variety of domains.

NetSci-X is the central winter conference within the field and brings together leading researchers and innovators to connect, meet, and establish interdisciplinary channels for collaboration. It is the largest and best known event in the area of network science.

This text demonstrates how ideas formulated by authors with different backgrounds are transformed into models, methods, and algorithms that are used to study complex systems across different domains and will appeal to researchers and students within in the field.

Neural Network Architectures. Examples Using Matlab

Download Neural Network Architectures. Examples Using Matlab

MATLAB has the tool Neural Network Toolbox that provides algorithms, functions, and apps to create, train, visualize, and simulate neural networks. You can perform classification, regression, clustering, dimensionality reduction, time-series forecasting, and dynamic system modeling and control. The toolbox includes convolutional neural network and autoencoder deep learning algorithms for image classification and feature learning tasks. To speed up training of large data sets, you can distribute computations and data across multicore processors, GPUs, and computer clusters using Parallel Computing Toolbox.

The more important features are the following:

•Deep learning, including convolutional neural networks and autoencoders

•Parallel computing and GPU support for accelerating training (with Parallel Computing Toolbox)

•Supervised learning algorithms, including multilayer, radial basis, learning vector quantization (LVQ), time-delay, nonlinear autoregressive (NARX), and recurrent neural network (RNN)

•Unsupervised learning algorithms, including self-organizing maps and competitive layers

•Apps for data-fitting, pattern recognition, and clustering

•Preprocessing, postprocessing, and network visualization for improving training efficiency and assessing network performance

•Simulink® blocks for building and evaluating neural networks and for control systems applications

Neural networks are composed of simple elements operating in parallel. These elements are inspired by biological nervous systems. As in nature, the connections between elements largely determine the network function. You can train a neural network to perform a particular function by adjusting the values of the connections (weights) between elements.

System Analyst Solutions: Enterprise Network Patching From Windows Command Line

Download System Analyst Solutions: Enterprise Network Patching From Windows Command Line

This book will show you how load software on remote workstations throughout your Enterprise network from your Desktop PC! This gives a System Analyst a backup solution to loading software, making configuration changes, and accessing different information from any PC on their domain without expensive software. This book covers WMIC and PSEXEC (WMIC comes pre-built, and PSEXEC is available free of charge. So download this book today!

Network Programming With Go: Essential Skills For Using And Securing Networks

Download Network Programming With Go: Essential Skills For Using And Securing Networks

Dive into key topics in network architecture and Go, such as data serialization, application level protocols, character sets and encodings. This book covers network architecture and gives an overview of the Go language as a primer, covering the latest Go release.

Beyond the fundamentals, Network Programming with Go covers key networking and security issues such as HTTP and HTTPS, templates, remote procedure call (RPC), web sockets including HTML5 web sockets, and more.

Additionally, author Jan Newmarch guides you in building and connecting to a complete web server based on Go. This book can serve as both as an essential learning guide and reference on Go networking.

What You Will Learn

Master network programming with Go

Carry out data serialization

Use application-level protocols

Manage character sets and encodings

Deal with HTTP(S)

Build a complete Go-based web server

Work with RPC, web sockets, and more

Who This Book Is For

Experienced Go programmers and other programmers with some experience with the Go language.