Shari R. Waldstein (ed.); Matthew F. Muldoon (ed.); Dennis H. Novack (ed.); Herbert Ochitill (ed.); Steven Cole (ed.) Springer (2099) Saatavuus: Tulossa! Kovakantinen kirja
Simon Oliver; Robert Lee Williams; Sara Novic; Matthew Loftus; Arlie Coles; David Schaengold; Hannah Arendt; J L Wall Plough Publishing House (2024) Saatavuus: Hankintapalvelu Pehmeäkantinen kirja
Explore supervised and unsupervised learning techniques and add smart features to your applications
About This Book
* Leverage machine learning techniques to build real-world applications * Use the Accord.NET machine learning framework for reinforcement learning * Implement machine learning techniques using Accord, nuML, and Encog
Who 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.
What You Will Learn
* Learn to parameterize a probabilistic problem * Use Naive Bayes to visually plot and analyze data * Plot a text-based representation of a decision tree using nuML * Use the Accord.NET machine learning framework for associative rule-based learning * Develop machine learning algorithms utilizing fuzzy logic * Explore support vector machines for image recognition * Understand dynamic time warping for sequence recognition
In Detail
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.
Style and approach
A step-by-step approach to learning machine learning concepts and techniques with practical implementations
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