The Cerebros package is an ultra-precise Neural Architecture Search (NAS) / AutoML that is intended to much more closely mimic biological neurons than conventional neural network architecture strategies.

Implementation of the paper "On the Asymptotic Mean Square Error Optimality of Diffusion Models."

PyTorch implementations of the beta divergence loss.

Our project focuses on forecasting photovoltaic (solar) power generation using a hybrid model of Gradient Boosting and LSTM. It predicts solar output with high accuracy, optimizing energy usage, improving grid stability, and enhancing renewable energy integration.

Implementation of two new protocols in the Shuffle Model of Differential Privacy for the private summation of vector-valued messages

Super Resolution's the images by 3x using CNN

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Classifying whether the credit card transaction is fraudulent or not using Logistic Regression

Practice using PyTorch include data preprocessing, linear algebra, optimization, neural networks, CNNs, and more to cover ML and DL basics

This repo houses a Jupyter Notebook which is intended to walk you through Gradient Descent Algorithm from scratch.

Explains how to use ARIMA model to forecast future production units, enabling informed decision-making and planning in the electric and gas utilities sector.

The House Price Prediction System is a comprehensive project aimed at predicting housing prices based on various attributes using advanced data analysis and machine learning techniques.

The objective is to analyze flight delays in the United States. Data from airlines, airports, and runways will be collected and processed. Machine learning models will be built using logistic regression, decision trees, and XGB classifiers. Visualizations will be created in Tableau, and Excel dashboards and SQL queries will be used for analysis.

A Mathematical Intuition behind Linear Regression Algorithm

Program for non-planar camera calibration, mean square error, RANSAC algorithm, and testing with & without noisy data using extracted 3D world and 2D image feature points.

This repository utilizes time series analysis to predict natural gas prices, aiding informed decisions in the energy market. Through meticulous data preprocessing, visualization, and ARIMA modeling, it provides accurate forecasts. With regression and interpolation techniques, it offers deeper insights for stakeholders, enabling proactive strategies

This project is designed to extract sales data from a PostgreSQL database, process it, and use a Random Forest model to predict sales quantities. It also visualizes real and predicted sales for better understanding.

A dynamic Predictive Maintenance system that auto-detects dataset type and uses ANN for failure classification and LSTM for RUL forecasting. Optimized with Adam and Early Stopping, the project includes a Streamlit web interface for real-time model training and machine health predictions.

Implementation of different optimization algorithms. This was done as a research project for the MSc. in Computer Engineering.

Comparison of common loss functions in PyTorch using MNIST dataset

Tutorial - Implementing a Linear Regression from Scratch - MSE & Gradient Descent - 2025

Value to Business :: Using this Regression model, the decision-makers will able to understand the properties of various products and stores which play an important and key role in optimizing the Marketing efforts and results in increased sales.

Learning Project ML - Diabetes Prediction

This project provides tools to search for datasets on Kaggle, download and preprocess them, and perform predictions using a Linear Regression model. It includes interactive text-based user interfaces built with `curses`.

This code demonstrates how to integrate Apache Beam with scikit-learn datasets and perform simple data transformations. It loads the Linnerud dataset from scikit-learn, converts it into a Pandas DataFrame for easier manipulation.

Self implementation of the Gradient Descent

Applied Multivariable Linear Regression on Iris Dataset

Regression based project with mean squared error as evaluation metric

Python images vector quantizer lossy compressor and decompressor.

Forecast Airlines Passengers data set. Prepare a document for each model explaining how many dummy variables you have created and RMSE value for each model. Finally which model you will use for Forecasting