IJCAI 2021, "Comparing Kullback-Leibler Divergence and Mean Squared Error Loss in Knowledge Distillation"

MDL Complexity computations and experiments from the paper "Revisiting complexity and the bias-variance tradeoff".

PyTorch implementations of the beta divergence loss.

Bayesian Regression of Latent Source Modelling for Predicting Price Variation of Bitcoin

This project predicts used car prices using a feedforward neural network regression model implemented in PyTorch. Features include car age, mileage, and other attributes. The pipeline supports feature normalization, train/validation/test splitting, and visualization of training and validation loss curves.

Machine Learning for Data 3141 Reichman University Spring 2022 - 6 Homework Projects

What Happens if We Use a Mean Squared Error Loss for Binary Classification?

This project used various machine learning algorithms to predict rainfall.

Forecasting MCTS Variant Outcomes Across Board Games

Intrusion Detection System for MQTT Enabled IoT.

Neural Network implemented with different Activation Functions i.e, sigmoid, relu, leaky-relu, softmax and different Optimizers i.e, Gradient Descent, AdaGrad, RMSProp, Adam. You can choose different loss functions as well i.e, cross-entropy loss, hinge-loss, mean squared error (MSE)

All things around ... Regression

A collection of TypeScript-based machine learning helpers.

A small code for understanding linear regression.

CERN Electron collision data- Invariant mass prediction

Laptop Price Prediction using Machine Learning. This project analyzes laptop specifications like RAM, CPU, storage, and GPU to predict prices using data preprocessing, EDA, and a Linear Regression model.

💎 'The Linear Regression Challenge: Diamonds Price Prediction' @ironhack Data Analytics Bootcamp

Forecasting % Baseline, a measure of solar panel energy output, using weather and solar irradiance data.

This repository contains a Jupyter Notebook that implements PCA (Principal Component Analysis) from scratch for facial recognition. It demonstrates the steps involved in PCA, including eigenface computation and accuracy comparisons for different components.

This project provided practice with logistic regression and the cost functions MSE and log loss

Ein System zur Umsetzung des automatisierten Matchingprozesses im Rahmen des Projekts Lehr:werkstatt der Universität Augsburg. Hinter SSO.

Create a simple, univariate linear regression model that predicts the salary from a person's experience (measured in years), using the gradiant descent algorithm.

The "Advertising Impact Analysis" project aims to analyze the relationship between advertising expenditure across different channels (such as TV, radio, online) and its impact on sales or revenue.

A regression model to predict housing prices based on various features.

Create a prototype for a machine learning model to predict the amount of gold recovered from gold ore.

A machine learning project that predicts car prices based on a dataset.

Implements a multi-level approach to image encryption and signing using post-quantum cryptographic techniques, with a focus on the FALCON (Fast-Fourier Lattice-based Compact Signatures over NTRU) algorithm for digital signatures.

Using machine learning to discover the best location for Oily Giant to open their next well, based on reserve volume and profit

A Python implementation of Gradient Descent for solving Multiple Linear Regression. This project demonstrates how the algorithm is used to minimize the Mean Squared Error (MSE) cost function and optimize the regression coefficients.

In this Notebook, MSE of three models (LinearRegression, Polynomial, and 3-layer Neural Network using Keras) has calculated and compared