Initial implementation utilising AARNN principles
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Updated
Feb 27, 2023 - C
Initial implementation utilising AARNN principles
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).
Animation Tweening of 3D vertex data using a Feed-Forward Neural Network.
Reinforcement Learning / Q Learning
A basic tic tac toe game using minimax algorithm
🤖 Neural real estate appraisal calculator is implemented in C programming language. The application is designed to calculate the assessment of real estate using a single neuron 🤖
An innovative and sperimental 64 bit Bare Metal Operative System based on Arm Cortex A53 MPU and developed in ASM/C using ArmV7/ArmV8 Instruction Set Architecture. This project aims to develop a new generation OS that exploit Machine Learning and Artificial Intelligence in order to improve fault rate of high fault rate processes.
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).
Solving missionary cannibal problem using different search strategies in C
Server to play with the Reversi AI https://github.com/marcluque/Reversi-AI
NeverCruncher AI - Bot that solves Neverball levels using Reinforced Learning.
🧠 The Neural converter is implemented in C programming language. The application is designed to transform scores in 5- and 12-point grading systems using a single neuron 🧠
Project for the Artificial Intelligence course @cse.uoi.gr
Artificial Intelligence algorithms.
Projeto que utiliza a base de dados Iris para calcular a acurácia e a função de perda de um modelo de aprendizado de máquina. Focado em análise de desempenho e avaliação de modelos.
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