Programming Microsoft Agent Applications Using Visual Basic (Mps)

Computers & Technology

Cole, Matt R.

Mowery, Robert H., III

Programming Languages

Microsoft Pr

Explore supervised and unsupervised learning techniques and add smart features to your applicationsKey FeaturesLeverage machine learning techniques to build real-world applicationsUse the Accord.NET machine learning framework for reinforcement learningImplement machine learning techniques using Accord, nuML, and EncogBook DescriptionThe necessity for machine learning is everywhere, and most production enterprise applications are written in C# using tools such as Visual Studio, SQL Server, and Microsoft Azur2e. Hands-On Machine Learning with C# uniquely blends together an understanding of various machine learning concepts, techniques of machine learning, and various available machine learning tools through which users can add intelligent features.These tools include image and motion detection, Bayes intuition, and deep learning, to C# .NET applications.Using this book, you will learn to implement supervised and unsupervised learning algorithms and will be better equipped to create excellent predictive models. In addition, you will learn both supervised and unsupervised forms of regression, mainly logistic and linear regression, in depth. Next, you will use the nuML machine learning framework to learn how to create a simple decision tree. In the concluding chapters, you will use the Accord.Net machine learning framework to learn sequence recognition of handwritten numbers using dynamic time warping. We will also cover advanced concepts such as artificial neural networks, autoencoders, and reinforcement learning.By the end of this book, you will have developed a machine learning mindset and will be able to leverage C# tools, techniques, and packages to build smart, predictive, and real-world business applications.What you will learnLearn to parameterize a probabilistic problemUse Naive Bayes to visually plot and analyze dataPlot a text-based representation of a decision tree using nuMLUse the Accord.NET machine learning framework for associative rule-based learningDevelop machine learning algorithms utilizing fuzzy logicExplore support vector machines for image recognitionUnderstand dynamic time warping for sequence recognitionWho This Book Is ForHands-On Machine Learning with C#is forC# .NETdevelopers who work on a range of platforms from .NET and Windows to mobile devices. Basic knowledge of statistics is required.Table of ContentsMachine Learning BasicsReflectInsight - Real-Time MonitoringBayes Intuition - Solving the 'Hit and Run' Mystery and Performing Data AnalysisRisk versus Reward - Reinforcement LearningNavigating the obstacle course with Fuzzy LogicColor Blending - Self-Organizing Maps and Elastic Neural NetworksFacial and Motion Detection - Imaging Filters and Motion Area Highlighting with Accord.NetEncyclopedias and NeuronsShould I Take the Job - Decision Trees in ActionDeep Belief Networks Using SharpRBMMicro-Benchmarking and Activation FunctionsIntuitive Deep Learning in C# .NetQuantum Computing - The Future

Hands-On Machine Learning with C#

Computer Science

Packt Publishing

Build enterprise-grade microservice ecosystems with intensive case studies using C# Key Features<ul>
<li>Learn to build message-based microservices</li>
<li>Packed with case studies to explain the intricacies of large-scale microservices</li>
<li>Build scalable, modular, and robust architectures with C#</li>
</ul>Book DescriptionC# is a powerful language when it comes to building applications and software architecture using rich libraries and tools such as .NET.
This book will harness the strength of C# in developing microservices architectures and applications.
This book shows developers how to develop an enterprise-grade, event-driven, asynchronous, message-based microservice framework using C#, .NET, and various open source tools. We will discuss how to send and receive messages, how to design many types of microservice that are truly usable in a corporate environment. We will also dissect each case and explain the code, best practices, pros and cons, and more.
Through our journey, we will use many open source tools, and create file monitors, a machine learning microservice, a quantitative financial microservice that can handle bonds and credit default swaps, a deployment microservice to show you how to better manage your deployments, and memory, health status, and other microservices. By the end of this book, you will have a complete microservice ecosystem you can place into production or customize in no time.What you will learn<ul>
<li>Explore different open source tools within the context of designing microservices</li>
<li>Learn to provide insulation to exception-prone function calls</li>
<li>Build common messages used between microservices for communication</li>
<li>Learn to create a microservice using our base class and interface</li>
<li>Design a quantitative financial machine microservice</li>
<li>Learn to design a microservice that is capable of using Blockchain technology</li>
</ul>Who This Book Is ForC# developers, software architects, and professionals who want to master the art of designing the microservice architecture that is scalable based on environment. Developers should have a basic understanding of.NET application development using C# and Visual StudioTable of Contents<ol>
<li>Let's Talk Microservices and Tools</li>
<li>ReflectInsight – Microservice Logging Redefined</li>
<li>MicrCreating a Base Microservice and Interface</li>
<li>Designing a Memory Management Microservice</li>
<li>Designing a Deployment Monitor Microservice</li>
<li>Designing a Scheduling Microservice</li>
<li>Designing an Email Microservice</li>
<li>Designing a File Monitoring Microservice</li>
<li>Creating a Machine Learning Microservice</li>
<li>Creating a Quantitative Financial Microservice</li>
<li>Trello Microservice – Board Status Updating</li>
<li>Microservice Manager – The Nexus</li>
<li>Creating a Blockchain Bitcoin Microservice</li>
<li>Adding Speech and Search to your Microservice</li>
<li>Appendix- Best Practices</li>
</ol>

Hands-On Microservices with C#: Designing a real-world, enterprise-grade microservice ecosystem with the efficiency of C# 7

Programming

Get hands on with Kelp.Net, Microsoft's latest Deep Learning framework Key Features<li> Deep Learning Basics</li><li> The ultimate Kelp.Net reference guide</li><li> Develop state of the art deep learning applications</li><li> C# Deep Learning code </li><li> Develop advanced deep learning models with minimal code</li> <li> Develop your own advanced Deep Learning models</li> <li> Loading and Saving Deep Learning Models</li> <li> Comprehensive Kelp.Net reference</li> <li> Sample Deep Learning Models and Tests</li> <li> OpenCL Reference</li> <li> Easily add deep learning to your applications</li> <li> Many sample models and tests</li> <li> Intuitive and user friendly</li></ul> Description Deep Learning with Kelp.Net is the ultimate reference for C# .Net developers who are passionate about deep learning. Readers will learn all the skills necessary to develop powerful, scalable and flexible deep learning models from a fluid and easy to use API. Upon completing the book the reader will have all the tools necessary to add powerful deep learning capabilities to their new or existing applications. What you will learn <li> In-depth knowledge of Kelp.Net</li><li> How to develop Deep Learning models</li><li> C# Deep Learning programming </li><li> Open-Computing Language (OpenCL)</li> <li> Loading and saving Deep Learning models</li> <li> How to develop and use activation functions</li> <li> How to test Deep Learning models</li> Who This Book is For This book targets C# .Net developers who are passionate about deep learning yet want to do so from an easy and intuitive API. Table of Contents <li> Introduction</li><li> ML/DL Terms and Concepts</li><li> Deep Instrumentation</li><li> Kelp.Net Reference</li><li> Loading and Saving Models</li><li> Model Testing and Training</li><li> Sample Deep Learning Tests</li><li> Creating Your Own Deep Learning Tests</li><li> Appendix A: Evaluation Metrics</li><li> Appendix B: OpenCL</li></ol> About the Author Matt R. Cole is a seasoned developer and published author with over 30 years' experience in Microsoft Windows, C, C++, C# and .Net. He is the owner of Evolved AI Solutions, a premier provider of advanced Machine Learning/Bio-AI technologies. He developed the first enterprise grade MicroService framework written completely in C# and .Net, which is used in production by a major hedge fund in NYC. He also developed the first Bio Artificial Intelligence framework which completely integrates mirror and canonical neurons. He continues to push the limits of Machine Learning, Biological Artificial Intelligence, Deep Learning and MicroServices. In his spare time Matt loves to continue his education and contribute to open source efforts such as Kelp.Net. His Website www.evolvedaisolutions.com His LinkedIn Profile www.linkedin.com/in/evolvedai/ His Blog www.evolvedaisolutions.com/blog.html

Deep Learning with C#, .Net and Kelp.Net: The Ultimate Kelp.Net Deep Learning Guide

BPB Publications

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Books by Cole, Matt R.