Hands-On Machine Learning with C#

Explore supervised and unsupervised learning techniques and add smart features to your applications

Hands-On Machine Learning with C#

Matt R. Cole
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Explore supervised and unsupervised learning techniques and add smart features to your applications
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Book Details

ISBN 139781788994941
Paperback274 pages

Book 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.

Table of Contents

Chapter 2: ReflectInsight – Real-Time Monitoring
Chapter 3: Bayes Intuition – Solving the Hit and Run Mystery and Performing Data Analysis
Chapter 5: Fuzzy Logic – Navigating the Obstacle Course
Chapter 6: Color Blending – Self-Organizing Maps and Elastic Neural Networks
Chapter 7: Facial and Motion Detection – Imaging Filters
Chapter 8: Encyclopedias and Neurons – Traveling Salesman Problem
Chapter 11: Microbenchmarking and Activation Functions
Chapter 13: Quantum Computing – The Future

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

Authors

Table of Contents

Chapter 2: ReflectInsight – Real-Time Monitoring
Chapter 3: Bayes Intuition – Solving the Hit and Run Mystery and Performing Data Analysis
Chapter 5: Fuzzy Logic – Navigating the Obstacle Course
Chapter 6: Color Blending – Self-Organizing Maps and Elastic Neural Networks
Chapter 7: Facial and Motion Detection – Imaging Filters
Chapter 8: Encyclopedias and Neurons – Traveling Salesman Problem
Chapter 11: Microbenchmarking and Activation Functions
Chapter 13: Quantum Computing – The Future

Book Details

ISBN 139781788994941
Paperback274 pages
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