abhishek-iyer1 Goto Github PK

abhishek-iyer1.github.io

Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes

ai-generated-iqa

cartpole-deep-reinforced-learning

We implement a DQN Agent using a Boltzmann Q Policy to tame the Cart Pole problem in the discrete action space. The method is tested against the baseline of making random movements and is implemented using the OpenAI Gym.

drone-network-iot-nodemcu-azure

This is the end semester project done for the IOT (Internet of Things) course at BITS Pilani, Pilani Campus. In this project, we develop an end to end drone management and networking system using Node MCU, Ublox-NEO6M GPS Sensor, Flask, PythonAnywhere and Azure.

emotion-classifcation-eeg-seed-ensemble

Using Deep Learning for Emotion Classification on EEG signals (SEED Dataset). CNN, RNN, Hybrid model, and Ensemble

groundingdino

Official implementation of the paper "Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection"

lane-detection

Detection and segmentation of lanes for assisting in automated driving using UNET and OpenCV

marine-search-and-rescue

markdown-cheatsheet

Markdown Cheatsheet for Github Readme.md

medmnist

[ISBI'21] MedMNIST Classification Decathlon: A Lightweight AutoML Benchmark for Medical Image Analysis

nnfl-assignment1-pso-nn

This is the code to the first assignment of the Neural Network and Fuzzy Logic course taught at BITS Pilani. In this data preprocessing, a basic NN, and the PSO algorithm is implemented from scratch. The code is self explanatory.

pytorch-examples

Simple examples to introduce PyTorch

retro-car-game

Creating a replica of the retro car game using pygame

siamese-mask-rcnn

Siamese Mask R-CNN model for one-shot instance segmentation

smart-colour-extraction

This project is aimed at automatically extracting important colours from a variety of images. The strength of this algorithm lies in detecting salient colours even when the occupy only a few pixels.

visualizing-feature-maps-cifar10

Training and evaluating a CNN model from scratch on the CIFAR10 dataset. Plotting layers, filters and feature maps to use as debugging tools. Implemented in Keras