Minmax (minimax) algorithm with Alpha-Beta (𝛼−𝛽, ab) Pruning optimization for the Checkers (Draughts) game in Python 3. Same minimax algorithm works for chess, tic tac toe or similar games.

Supervised Machine Learning Analysis Using Classification Models

Some DNN model optimization experiments and notebooks

Code of the paper "Evolutionary convolutional neural network for efficient brain tumor segmentation and overall survival prediction

This repository contains scripts to prune Wav2vec2 using a neuroevolution-based method. More details about this method can be found in the paper Compressing Wav2vec2 for Embedded Applications.

The objective of this project is to understand and implement the most commonly used Tree-pruning methods, Pre-pruning and Post-pruning.

A bot for Ultimate Tic-Tac-Toe game on https://theaigames.com/ and https://pub.theaigames.com/competitions/ultimate-tic-tac-toe

DenseNet & Resnet as Backbone to Perform Lahan Gambut Classification with Self-Attention Pruning Mechanism

Pneumo.ai is a Streamlit web app for detecting pneumonia from X-rays, leveraging CNN variants like AlexNet, VGG, and ResNet18, with ResNet18 leading with an accuracy of 0.89. It employs early stopping callbacks for efficient model training, visualizes training using TensorBoard, and optimizes inference time with pruning and quantization techniques.

Homework for Graduate Deep Learning Course

Keras-based Deep Learning to cleanse patient data and predict their chances of survival

Pytorch optimizer for nonsmooth, nonconvex, sparsity inducing, regularizers

Frequency Based Pruning (FBP) is a feature selection algorithm based upon maximizing the Youden J statistic. FBP intelligently enumerates through combinations of features, using the frequency of smaller patterns to prune away large regions of the solution space.

CPT_S 570 Machine Learning

leverage decision tree-based models for predicting credit approval.

AI agent capable of solving large instances of pipe puzzles. Project for Artificial Intelligence class.

IISc/CSA E0-294: Systems for Machine learning - Course project on employing causal insights in DNN model pruning and performance

A collection of multiple projects involving tasks such as classification, time series forecasting , regression etc. on a number of datasets using different machine learning algorithms such as random forest, SVM, Naive Bayes, Ensemble, perceptron etc in addition to data cleaning and preparation.