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Nishant Bhushan's Projects

burger-rampage icon burger-rampage

You control a delicious, juicy burger and shoot small burgers at teddy bears. But beware, they will shoot their kids back at you. This game is not for vegans.

dd-command-on-linux icon dd-command-on-linux

dd is a command-line utility used to copy data to and from files. Since Linux treats many external devices (including USB drives) as files, this makes dd very powerful. For example, the tool can be used to create a backup image of your hard drive and store it as a file which can be uploaded to cloud storage. dd could also be used to directly clone one drive to another, write a bootable iso image to a USB drive, and much more. implementation will copy data from an input file to an output file in a manner specified by its arguments.

lab3 icon lab3

Project skeleton for Lab 3.

london-skybox-and-teapot icon london-skybox-and-teapot

A simple WebGL Implementation of a skybox that consists of 6 images texture mapped into a cube. Also includes a rendering of a teapot made of triangular meshes read from an OBJ file format to demonstrate 3 different types of shading: Blinn-Phong, Reflection and Refraction. You must first establish a local http server. If you have node.js you can do this with the command "httpserver" and then type "localhost:8080" into your browser.

naive-bayes-classifier icon naive-bayes-classifier

Naive Bayes Classifer that can read a txt file with '.' to color a pixel and look like a number. In this project, I train a Naive Bayes classifer to be able to predict the number represented by each test image.

privategpt icon privategpt

Interact privately with your documents using the power of GPT, 100% privately, no data leaks

ray-tracer---part-1 icon ray-tracer---part-1

Ray Tracer that can project a ray orthographically or perspectively and test for the rays intersection with spheres, triangles and planes. Colors for the pixel are generated using Phong shading model. Anti-aliasing (Multi-Sampled jittering) helps prevent the staircase like appeareance of curved edges. It smooths the surfaces by casting many rays, adding up the colors at each point then dividing by the number of rays. Shadows are generated by assuming light as point sources. Part 2 of this project implements a Bounding Volume Hierarchy (BVH) and adds support for reading triangular meshes from a .obj file. You can find it here: https://github.com/nishantb15/MP2---Part-2---BVH-and-Mesh

ray-tracer-part-2-bvh-and-mesh icon ray-tracer-part-2-bvh-and-mesh

This ray tracer builds from my previous ray tracer (https://github.com/nishantb15/Ray-Tracer---Part-1) which implemented simple shapes, hard shadows, Phong Reflection from a point light source, Multi-Jittered Sampling using n-rooks strategy (Anti-aliasing) and Orthographic and Perspective projection with a moveable camera. This ray tracer adds a Bounding Volume Hierarchy (BVH) which is an accelerated structure, in this case, used to generate an image containing 100,000 spheres which would take days to render without a BVH. More information about the BVH is contained in the BVHTable.pdf file. Support is also included to read a .obj file and import a triangular mesh into the scene. The mesh can then be shaded using per-vertex normals as opposed to per-face (1 normal per face). The pictures generated using BVH are generated using 1 for the number of samples in multi-jittered sampling (essentially no anti-aliasing) and no shadows to save cost. The purpose of this project was to test the efficiency of the BVH in generating an enormous amount of spheres and triangles. Part 3 Can be found at: https://github.com/nishantb15/Ray-Tracer-Part-3-Reflection-Refraction-And-Area-Lights

reinforcement-learning icon reinforcement-learning

In the Mountain Car environment, a car is on a one-dimensional track, positioned between two mountains. The goal is to drive up the mountain on the right; however, the car's engine is not strong enough to scale the mountain in a single pass. Therefore, the only way to succeed is to drive back and forth to build up momentum. The car's state is described by the observation space which is a 1D vector containing car's horizontal position and velocity. The car can take 3 actions: Left, Do Nothing, and Right. The episode ends when either the car reaches the flag on the right hill or it fails in 200 moves (or frames). All the move that fails to reach the flag will get reward -1. Therefore, in the episode where the car fails to reach to goal, the total reward would be -200. The entire episode will be scored as 1.0 if the car reaches the goal (total_reward>−200) or 0.0 otherwise.

robot-arm icon robot-arm

A robotic arm that is able to use BFS to avoid the red obstacles and touch only the blue ones.

salesgpt icon salesgpt

Context-aware AI Sales Agent to automate sales outreach.

simple-java-maven-app icon simple-java-maven-app

For an introductory tutorial on how to use Jenkins to build a simple Java application with Maven.

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