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This is a repository for CS4ML. It is a general framework for active learning in regression problems. It approximates a target function arising from general types of data, rather than pointwise samples.
Example machine learning applications for the determination of the residual yield force of corroded steel bars tested under monotonic tensile loading. Data is collected from 26 experimental programs avaialbe in the literature.
Implementação do Trabalho de conclusão de curso, com o tema definido "Aproximação da cinemática inversa de um robô manipulador didático através de algoritmos de aprendizado de máquina"
This project is a Jupyter Notebook that analyzes how a regression model can be tuned to predict the stock market prices of Tesla (TSLA). The objective was to create a prediction algorithm to forecast the closing price of Tesla stock on a specific date.
This repository implements a custom artificial neural network in PyTorch, achieving 87% accuracy on a regression dataset (outperforming Keras). It provides a hands-on approach to building neural networks from scratch while leveraging PyTorch for efficient training.