Implementation of regression algorithms in machine learning with c# 🤓.
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Updated
Jul 20, 2022 - C#
Machine learning is the practice of teaching a computer to learn. The concept uses pattern recognition, as well as other forms of predictive algorithms, to make judgments on incoming data. This field is closely related to artificial intelligence and computational statistics.
Implementation of regression algorithms in machine learning with c# 🤓.
Genetic Algorithm Using Strings In multiple languages
MedicalInformationAnalyzer extracts and labels relevant medical information from unstructured texts such as doctor's notes, discharge summaries, clinical documents, and electronic health records. It is a .NET console application.
A simple console game built on C# to introduce the concept of Machine Learning
Examples written on .Net Core from book Machine Learning with TensorFlow: Nishant Shukla
Machine Learning Applications I have produced using the Unity Game Engine will have their code samples placed here.
A from-scratch basic backpropagation neural network implemented in C#.
Implementation of k-means clustering algorithm
The project aims to employ the power of machine learning to identify bird species from the real-time images captured
Sample-based learning implementation to classify answers of open questions in surveys (Semantic Field) given a .csv with seed words.
Simple MultiLayerPerceptron with dummy multidimensionnal NDArray backend written in C#
An artificial intelligence vehicle collision avoidance system demonstrated in a simulation that uses neural networks paired with a genetic algorithm and sensors.
Implementation of the AQ11 machine learning algorithm in C#. Used on the medical data to predict whether a stroke is possible.
C# .NET 5.0 Neural Network, GA/ML Library
There are some basic implementations of K-nearest neighbors and Naive Bayes classifiers.
The Perceptron is a machine learning algorithm that provides classified results for computing. It dates back to the 1950s and represents a fundamental example of how machine learning algorithms work to develop data.
The GitHub Repository For The Research Paper "Supervised Data in Backpropagation and Genetics Learning Algorithms"
This genetic algorithm uses natural selection and mutation to generate a random 'being' which fulfill a determined goal.