Abhishek-EE

stanford-cs-230-deep-learning

VIP cheatsheets for Stanford's CS 230 Deep Learning

Gaussian-Mixture-Models

Gaussian Mixture Model (GMM) and Gaussian Mixture Regression (GMR) implemented purely on numpy

Monte-Carlo-Simulator

Monte Carlo Simulator for Bit Error Rate Estimation in Digital Communication

Text-Similarity-Score-Generator

This is a Text Similarity Score Generator. It takes in two different texts and compares how similar they are. To calculate the similarity score I am using Vector Space Model. This model creates a vector Space where each dimension represents a single word. Words are taken from all the texts that are considered. One document is a single vector space. Each dimension of a single document vector represents how often this word appears in the text.To compare two documents a cosine similarity is used. This generates a value between 0 and 1, 0 meaning no similarity and 1 meaning perfect match.

Face_Recognition

Compared the performance of Gaussian model, Mixture of Gaussian model, t-distribution, Mixture of t-distribution, Factor Analysis and Mixture of Factor Analyzer on the task of facial recognition under the Generative modeling scheme.

Polynomial_Regression

A simple code to understand polynomial regression for curve fitting

Wilting_Classification_Using_GMM

This was my Project for ECE 542

BabySitting-Method-for-tuning-a-CNN-and-NN

Multilayered_Perceptron

Implementation of Multilayered Perceptron

Path-Planning-Simulator

This project simulates pathfinding for a Roomba-like robot in a pre-mapped environment. The environment is represented by an occupancy grid where each cell indicates the presence of an obstacle or free space. The program calculates a collision-free path from a starting position to a goal position.