Hands-On Machine Learning with C# (eBook, ePUB)
Build smart, speedy, and reliable data-intensive applications using machine learning
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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 vario...
Explore supervised and unsupervised learning techniques and add smart features to your applications
Key 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 Description
The 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 for
Hands-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.
Matt R. Cole is a seasoned developer with 30 years' experience in Microsoft Windows, C, C++, C#, and .NET. He previously wrote a speech and audio VOIP system for NASA for use with the Space Shuttle and a space station. He is the owner of Evolved AI Solutions, a premier provider of advanced ML/Bio-AI technologies. He developed the first enterprise-grade microservice framework (written fully in C# and .NET) used by a major hedge fund in NYC and he also developed the first Bio-AI Swarm framework, which fully integrates mirror and canonical neurons.
Key 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 Description
The 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 for
Hands-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.
Matt R. Cole is a seasoned developer with 30 years' experience in Microsoft Windows, C, C++, C#, and .NET. He previously wrote a speech and audio VOIP system for NASA for use with the Space Shuttle and a space station. He is the owner of Evolved AI Solutions, a premier provider of advanced ML/Bio-AI technologies. He developed the first enterprise-grade microservice framework (written fully in C# and .NET) used by a major hedge fund in NYC and he also developed the first Bio-AI Swarm framework, which fully integrates mirror and canonical neurons.
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