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Resoruce to help you to prepare for your comming data science interviews
🎮 🎲 A wonderful list of Game Development resources.
One framework for creating powerful cross-platform games.
This is literally a game framework, based on Unity game engine. It encapsulates commonly used game modules during development, and, to a large degree, standardises the process, enhances the develop…
A repository listing out the potential sources which will help you in preparing for a Data Science/Machine Learning interview. New resources added frequently.
A Collection of Cheatsheets, Books, Questions, and Portfolio For DS/ML Interview Prep
Data science interview questions and answers
Curated coding interview preparation materials for busy software engineers
Machine Learning and Computer Vision Engineer - Technical Interview Questions
This repo is meant to serve as a guide for Machine Learning/AI technical interviews.
Machine Learning Interviews from FAANG, Snapchat, LinkedIn. I have offers from Snapchat, Coupang, Stitchfix etc. Blog: mlengineer.io.
GPS points will be predefined for robots to navigate to the destination avoiding obstacles. Part of University Mars Rover Challenge - 2019.
Python sample codes and textbook for robotics algorithms.
This repo contains a number of algorithms that I experimented with in order to (approximately) solve the Traveling Salesman Problem with Time Windows.
An easy-to-use Python library for processing and manipulating 3D point clouds and meshes.
Auto-encoding & Generating 3D Point-Clouds.
A curated list of awesome places to learn and/or practice algorithms.
Studying for a tech interview sucks. Here's an open source cheat sheet to help
An Open-Source Collection of Flash Cards to Help You Preparing Your Algorithms & Data Structures and System Design Interviews 💯
A beautiful, simple, clean, and responsive Jekyll theme for academics
PU-GAN: a Point Cloud Upsampling Adversarial Network, ICCV, 2019
Production Data Science: a workflow for collaborative data science aimed at production
my octave exercises for 2011 stanford machine learning class, posted after the due date of course
Tutorials for Fall 2018