Collection of various algorithms in mathematics, machine learning, computer science, physics, etc implemented in C for educational purposes.
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Updated
Aug 7, 2024 - 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.
Collection of various algorithms in mathematics, machine learning, computer science, physics, etc implemented in C for educational purposes.
An Embedded Computer Vision & Machine Learning Library (CPU Optimized & IoT Capable)
darknet neural network addon for openFrameworks
This is a basic implementation of a neural network for use in C and C++ programs. It is intended for use in applications that just happen to need a simple neural network and do not want to use needlessly complex neural network libraries.
Multi-mode Fault Diagnosis Datasets with TE process (MMFDD-TEP) can be used for the purpose of comparison studies or validation of algorithms
Implementation of the Perceptron with Perceptron Learning.
Regularization Paths for Huber Loss Regression and Quantile Regression Penalized by Lasso or Elastic-Net
MicroAI™ is an AI engine that can operate on low power edge and endpoint devices. It can learn the pattern of any and all time series data and can be used to detect anomalies or abnormalities, make one step ahead predictions/forecasts, and calculate the remaining life of entities (whether it is industrial machinery, small devices or the like).
Simple Recurrent Neural Network implementation.
Neural Network demonstration with Backpropagation learning and the Iris dataset.
Solution Paths of Sparse Linear Support Vector Machine with Lasso or ELastic-Net Regularization
HiPGMC is a highly efficient, parallel Graph-based Multi-view Clustering implementation.
Sentiment Analysis of Communication over Social Network
K-means ML algorithm implementation in C and SDL.
Codes for AAAI 2018 paper "Sensor-based Activity Recognition via Learning from Distributions".
Python code to reproduce our article "Toward faultless content-based playlists generation for instrumentals"
Animation Tweening of 3D vertex data using a Feed-Forward Neural Network.
AtomML™ is an AI engine that can operate on low power edge and endpoint devices. It can learn the pattern of any and all time series data and can be used to detect anomalies or abnormalities, make one step ahead predictions/forecasts, and calculate the remaining life of entities (whether it is industrial machinery, small devices or the like).