Abhishek Ranjan Singh's starred repositories
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
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.