Aditya Jain's Projects
8-Puzzle game, solvable using A*
A Simple Android Calculator with beautiful design
My website
Some basic AI/ML/DL algorithms implemented from scratch for understanding purposes.
:bulb: Minimal and clean examples of data structures and algorithms in Python
Apache spark is a big data analysis framework.
Make a cool intro for your Android app.
An Attention Based machine translation to convert human readable dates to machine readable dates.
This is a kaggle Challenge. Given a pair of images of 2 faces we have to determine whether they are related or not. Here I have used a Siamese network over VGG-facenet to tackle this problem.
:beers: awesome cheatsheet
A topic-centric list of high-quality open datasets in public domains. Propose NEW data ☛☛☛PR☛☛☛
It is a Data Science competition by Analytics Vidhya. Learn to do feature engineering etc.
A web-based demonstration of blockchain concepts.
Implementation of U-Net from paper "U-Net: Convolutional Networks for Biomedical Image Segmentation" to segment tumor in given MRI images.
Project code for cd0581 refresh taught by Giacomo Vianello
VGG Deep Convolutional Network fine tuned on Cat vs Dog dataset using Transfer Learning. VGG was trained on famous IMAGENET dataset.
Draw random shape on HTML5 canvas and let Deep ConvNet predict it.
ChatBot Workshop conducted at MIT pune
ChatterBot is a machine learning, conversational dialog engine for creating chat bots
A set of some Machine Learning utils frequently used by me while working as Data Scientist in Cognizant
Codes done in MIT Labs during Computer Engineering undergraduate program at SPPU from SE to BE.
A repository to maintain all programming assignments in Deep Learning Specialization of Coursera
A set of python scripts to extract cricket data from https://cricbuzz.com for analytics purpose.
A Dictionary ChatBot using AMAZON LEX, LAMBDA and OXFORD dictionary API
THIS MIRROR IS DEPRECATED -- New url: https://gitlab.com/libeigen/eigen
A Face Recognition Siamese Network implemented using Keras. Siamese Network is used for one shot learning which do not require extensive training samples for image recognition.