The Nest of Students

Artificial Neural Network and Machine Learning using MATLAB This course is uniquely designed to be suitable for both experienced developers seeking to make that jump to Machine learning or complete beginners who don’t understand machine learning and Artificial Neural Network from the ground up. In this course, we introduce a comprehensive training of multilayer perceptron neural networks or MLP in MATLAB, in which, in addition to reviewing the theories related to MLP neural networks, the practical implementation of this type of network in MATLAB environment is also fully covered. MATLAB offers specialized toolboxes and functions for working with Machine Learning and Artificial Neural Networks which makes it a lot easier and faster for you to develop a NN. At the end of this course, you’ll be able to create a Neural Network for applications such as classification, clustering, pattern recognition, function approximation, control, prediction, and optimization.

What you’ll learn: Develop a multilayer perceptron neural networks or MLP in MATLAB using Toolbox + Apply Artificial Neural Networks in practiceBuilding Artificial Neural Network Model + Knowledge on Fundamentals of Machine Learning and Artificial Neural Network + Understand Optimization methods + Understand the Mathematical Model of a Neural Network + Understand Function approximation methodology + Make powerful analysis + Knowledge on Performance Functions + Knowledge on Training Methods for Machine Learning.

A new era of complexity science is emerging, in which nature- and bio-inspired principles are being applied to provide solutions. At the same time, the complexity of systems is increasing due to such models like the Internet of Things (IoT) and fog computing. Will complexity science, applying the principles of nature, be able to tackle the challenges posed by highly complex networked systems?

Bio-Inspired Optimization in Fog and Edge Computing: Principles, Algorithms, and Systems is an attempt to answer this question. It presents innovative, bio-inspired solutions for fog and edge computing and highlights the role of machine learning and informatics. Nature- or biological-inspired techniques are successful tools to understand and analyze a collective behavior. As this book demonstrates, algorithms, and mechanisms of self-organization of complex natural systems have been used to solve optimization problems, particularly in complex systems that are adaptive, ever-evolving, and distributed in nature.

The chapters look at ways of enhancingto enhance the performance of fog networks in real-world applications using nature-based optimization techniques. They discuss challenges and provide solutions to the concerns of security, privacy, and power consumption in cloud data center nodes and fog computing networks. The book also examines how: The existing fog and edge architecture is used to provide solutions to future challenges. + A geographical information system (GIS) can be used with fog computing to help users in an urban region access prime healthcare. + An optimization framework helps in cloud resource management. + Fog computing can improve the quality, quantity, long-term viability, and cost-effectiveness in agricultural production. + Virtualization can support fog computing, increase resources to be allocated, and be applied to different network layers. + The combination of fog computing and IoT or cloud computing can help healthcare workers predict and analyze diseases in patients.

Various didactic elements facilitate orientation and working with the book: At the checkpoints, the most important aspects from each chapter are briefly summarized. In the freak knowledge section, more advanced aspects are addressed to whet the appetite for more. All examples are calculated with hand and Excel. Numerous applications and solutions as well as further data sets are available on the author's internet platform.