Official implement for "SOFTS: Efficient Multivariate Time Series Forecasting with Series-Core Fusion"(NeurIPS'24) in PyTorch.

Predicting Amsterdam house / real estate prices using Ordinary Least Squares-, XGBoost-, KNN-, Lasso-, Ridge-, Polynomial-, Random Forest-, and Neural Network MLP Regression (via scikit-learn)

This project provides implementations with Keras/Tensorflow of some deep learning algorithms for Multivariate Time Series Forecasting: Transformers, Recurrent neural networks (LSTM and GRU), Convolutional neural networks, Multi-layer perceptron

Modeling time series of electricity spot prices using Deep Learning.

Stock price prediction using ensemble MLP in PyTorch.

Repository for "Inverse Kinematics of Tendon Driven Continuum Robots using Invertible Neural Network" (CompAuto 2022)

Predicting the compressive strength of concrete using ML methods and Artificial Nueral Networks. Tools used in this project are Jupyter Notebook, UCI ML repository,Kaggle,Google colab.

用java寫的MLP，總程式不到一千行，可使用主流十幾種激勵函數。

This repo covers the basic machine learning regression projects/problems using various machine learning regression techniques and MLP Neural Network regressor through scikit learn library

Trabajos prácticos de la cátedra Inteligencia Artificial 2 de Ingeniería en Mecatrónica - UNCuyo

A lightweight neural network framework in C

Multi-Layer Perceptron, MLP, Regression

IMBD 2021 Data Regression by MLP

This project delves into the intriguing realm of how thoughts and information shared on platforms like Twitter wield influence over financial markets.

Repository of my master’s thesis "Development and evaluation of a model for predicting the state of health of traction batteries based on artificial neural networks"

Truck transport US industry analysis and diesel price forecasting model using ML and traditional Data Science approach.

Hydrodynamic image with the artificial lateral line using physics-informed informed neural networks and other proven methods in 2D dipole localization.

Ask a home buyer to describe their dream house, and they probably won't begin with the height of the basement ceiling or the proximity to an east-west railroad. With 79 explanatory variables describing (almost) every aspect of residential homes in Ames, Iowa, this competition challenges you to predict the final price of each home.

Building a Neural Network from scratch using Tensorflow