Book Image

Hands-On Machine Learning with C#

By : Matt R. Cole
Book Image

Hands-On Machine Learning with C#

By: Matt R. Cole

Overview of this book

<p>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.</p> <p>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.</p> <p>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.</p>
Table of Contents (14 chapters)
5
Fuzzy Logic – Navigating the Obstacle Course
6
Color Blending – Self-Organizing Maps and Elastic Neural Networks

Running our application

For now, let's start using our application with our default parameters. Simply click on the Start button and the learning will commence. Once this is complete, you will be able to click on the Show Solution button, and the learned path will be animated from start to finish.

Clicking on Start will begin the learning stage and continue until the black object reaches its goal:

Here you will see that as the learning progresses, we are sending the output to ReflectInsight to help us see and learn what the algorithm is doing internally. You see that for each iteration, different object positions are being evaluated, and so are their actions and rewards:

Once the learning is complete, we can click on the Show Solution button to replay the final solution. When complete, the black object will sit atop the red object:

Now let's look at the code from...