Related Projects

CoderCalendar - Unmaintained -> Read Notice

  •    Javascript

This project is not actively maintained anymore. Development of the extensions (both firefox & chrome) has been moved to this new repository. Any issues relating to extensions should be posted in the new repo. Android App and browser extensions for competitive programming enthusiasts. Shows a list of live & upcoming coding contests taking place in various popular competitive programming websites with the facility to add them to your google calender. Currently shows updates from Codechef , HackerEarth , Hackerrank, Topcoder and Codeforces.

Competitive-Programming - My solutions from different contests and online Judge.

  •    C++

There are solutions from 2009 until now; The old ones doesn't have the solution explanation. Almost all of them in c++.

interactive-coding-challenges - Interactive Python coding interview challenges (algorithms and data structures)

  •    Python

Overhauled to now include 120 challenges and solutions and added Anki flashcards.Also included are unit tested reference implementations of various data structures and algorithms.


judge0 - 🔥 The most advanced open-source online code execution system in the world.

  •    HTML

🔥 The most advanced open-source online code execution system in the world. Judge0 is a robust, scalable, and open-source online code execution system. You can use it to build a wide range of applications that need online code execution features. Some examples include competitive programming platforms, e-learning platforms, candidate assessment and recruitment platforms, online code editors, online IDEs, and many more.

hiring-without-whiteboards - ⭐️ Companies that don't have a broken hiring process

  •    Javascript

A listing of companies (or teams) that don't do "whiteboard" interviews. "Whiteboards" is used as a metaphor, and is a symbol for the kinds of CS trivia questions that are associated with bad interview practices. Whiteboards are not bad – CS trivia questions are. Using sites like HackerRank/LeetCode probably fall into a similar category. The companies and teams listed here use interview techniques and questions that resemble day-to-day work – for example pairing on a real world problem, or a paid/unpaid take home exercise. Read (and contribute to) our recommendations for ways to conduct better interviews.

competitive-data-science - Materials for "How to Win a Data Science Competition: Learn from Top Kagglers" course

  •    Jupyter

This repository contains programming assignments notebooks for the course about competitive data science.

AlgoSolution - Solutions for Problems of Algorithm Contests and Online Judges

  •    C++

Solutions for Problems of Algorithm Contests and Online Judges

CtCI-6th-Edition - Cracking the Coding Interview 6th Ed. Solutions

  •    Java

Solutions for Cracking the Coding Interview 6th Edition by Gayle Laakmann McDowell. Crowdsourcing solutions for every widely used programming language. Contributions welcome.

cocoa-programming-for-osx-5e - Solutions and errata for Cocoa Programming for OS X, 5th Edition

  •    Swift

This branch contains solutions and the companion guide for Swift 2.0. If you are using Swift 1.2, see the swift1-2 branch. This repository contains the solutions and errata for Cocoa Programming for OS X - The Big Nerd Ranch Guide, 5th Edition, by Aaron Hillegass, Adam Preble, and Nate Chandler.

Rippled - Decentralized cryptocurrency blockchain daemon implementing the XRP Ledger in C++

  •    C++

Ripple is a network of computers which use the Ripple consensus algorithm to atomically settle and record transactions on a secure distributed database, the Ripple Consensus Ledger (RCL). Because of its distributed nature, the RCL offers transaction immutability without a central operator. The RCL contains a built-in currency exchange and its path-finding algorithm finds competitive exchange rates across order books and currency pairs.

UFLDL-tutorial - Deep Learning and Unsupervised Feature Learning Tutorial Solutions

  •    Jupyter

These are solutions to the exercises up at the Stanford OpenClassroom Deep Learning class and Andrew Ng's UFLDL Tutorial. When I was solving these, I looked around for copies of the solutions so I could compare notes because debugging learning algorithms is often tedious in a way that isn't educational, but almost everything I found was incomplete or obviously wrong. I don't promise that these don't have bugs, but they at least give outputs within the range of the expected outputs for the assignments. I've attempted to make this Octave compatible, so that you can run this with free software. It seems to work, but the results are slightly different. One side effect of this is that I'm using fminlbfgs instead of minFunc. It ran for me with Octave 3.6.4; my understanding is that Octave 3.8 and newer versions aren't completely backwards compatible, so you may run into problems with the current version of octave. Pull requests welcome, of course.

deep-reinforcement-learning - Repo for the Deep Reinforcement Learning Nanodegree program

  •    Jupyter

This repository contains material related to Udacity's Deep Reinforcement Learning Nanodegree program. The tutorials lead you through implementing various algorithms in reinforcement learning. All of the code is in PyTorch (v0.4) and Python 3.

python-business-analytics - Python solutions to solve practical business problems.

  •    Jupyter

Animated Investment Management Research at Sov.ai — Sponsoring open source AI, Machine learning, and Data Science initiatives. A series looking at implementing python solutions to solve practical business problems. Share your own projects on this subreddit, r/datascienceproject. Every week we will look at hand picked businenss solutions. See the following google drive for all the code and github for all the data. If you follow the LinkedIn page, you would be able to see the lastest developments.

deep-learning-coursera - Deep Learning Specialization by Andrew Ng on Coursera.

  •    Jupyter

This repo contains all my work for this specialization. All the code base, quiz questions, screenshot, and images, are taken from, unless specified, Deep Learning Specialization on Coursera. As a CS major student and a long-time self-taught learner, I have completed many CS related MOOCs on Coursera, Udacity, Udemy, and Edx. I do understand the hard time you spend on understanding new concepts and debugging your program. There are discussion forums on most MOOC platforms, however, even a question with detailed description may need some time to be answered. Here I released these solutions, which are only for your reference purpose. It may help you to save some time. And I hope you don't copy any part of the code (the programming assignments are fairly easy if you read the instructions carefully), see the quiz solutions before you start your own adventure. This course is almost the simplest deep learning course I have ever taken, but the simplicity is based on the fabulous course content and structure. It's a treasure given by deeplearning.ai team.

aws-solutions-constructs - The AWS Solutions Constructs Library is an open-source extension of the AWS Cloud Development Kit (AWS CDK) that provides multi-service, well-architected patterns for quickly defining solutions

  •    TypeScript

The AWS Solutions Constructs library is an open-source extension of the AWS Cloud Development Kit (AWS CDK) that provides multi-service, well-architected patterns for quickly defining solutions in code to create predictable and repeatable infrastructure. The goal of AWS Solutions Constructs is to accelerate the experience for developers to build solutions of any size using pattern-based definitions for their architecture. The patterns defined in AWS Solutions Constructs are high level, multi-service abstractions of AWS CDK constructs that have default configurations based on well-architected best practices. The library is organized into logical modules using object-oriented techniques to create each architectural pattern model.






We have large collection of open source products. Follow the tags from Tag Cloud >>


Open source products are scattered around the web. Please provide information about the open source projects you own / you use. Add Projects.