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Andrei Oprisan's Projects

ai icon ai

NxN-puzzle solver - BFS, DFS, A*, Iterative Deepening A* / 2048 Agent - Adversarial Search / Linear Regression - Gradient Descent / Clustering for Image Seg. / Sudoka CSP - AC3

ai-papers icon ai-papers

ML/DL papers which I found useful. Lots of them.

algorithms-for-data-science icon algorithms-for-data-science

This repository contains my solutions for assignments given in class for CSOR W4246 Algorithms for Data Science-Fall 2019

analytics.js icon analytics.js

universal javascript analytics library for uniform multi-vendor support

apignite icon apignite

API builder, user management, analytics and management platform (PHP, jquery)

apkg icon apkg

A tool that could create an abstract package tree in a distribution-independent manner that can implement a variety of package management systems in package creation would be a true asset to Linux system administrators.

applied_machine_learning icon applied_machine_learning

This repository contains my solutions for assignments given in class for COMS W4995 Applied Machine Learning Spring 2020

appminotaur icon appminotaur

web site uptime and reliability monitoring with user response time and other analytics

askmore icon askmore

initial MVP with PHP/MySQL CodeIgniter from Boston Startup Weekend 2013

awesome-cto icon awesome-cto

A curated and opinionated list of resources for Chief Technology Officers, with the emphasis on startups

awesome-engineering icon awesome-engineering

A curated list of awesome engineering blogs, handbooks and open source repos from top technology companies

aws-appsync-survey-tool icon aws-appsync-survey-tool

Sample Survey Tool Progressive Web Application written with React, GraphQL, AWS AppSync & AWS Amplify

chat-with-mlx icon chat-with-mlx

Chat with your data natively on Apple Silicon using MLX Framework.

chatwoot icon chatwoot

Opensource alternative to Intercom, Drift, Zendesk, FreshChat 🔥💬

comsw4721_machinelearning_homework icon comsw4721_machinelearning_homework

Implementation of Machine Learning algorithms using numpy in Python. The exercise was done as part of homeworks given at Columbia's Machine Learning (COMS W4721) class conducted by Dr. John Paisley

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