Finding the perfect place to call your new home should be more than browsing through endless listings. RentHop makes apartment search smarter by using data to sort rental listings by quality. But while looking for the perfect apartment is difficult enough, structuring and making sense of all available real estate data programmatically is even harder. Two Sigma and RentHop, a portfolio company of Two Sigma Ventures, invite Kagglers to unleash their creative engines to uncover business value in this unique recruiting competition. Two Sigma invites you to apply your talents in this recruiting competition featuring rental listing data from RentHop. Kagglers will predict the number of inquiries a new listing receives based on the listing’s creation date and other features. Doing so will help RentHop better handle fraud control, identify potential listing quality issues, and allow owners and agents to better understand renters’ needs and preferences. Two Sigma has been at the forefront of applying technology and data science to financial forecasts. While their pioneering advances in big data, AI, and machine learning in the financial world have been pushing the industry forward, as with all other scientific progress, they are driven to make continual progress. This challenge is an opportunity for competitors to gain a sneak peek into Two Sigma's data science work outside of finance. Acknowledgments This competition is co-hosted by Two Sigma and RentHop (a portfolio company of Two Sigma Ventures, which is a division of Two Sigma Investments) to encourage creativity in using real world data to solve everyday problems.

leetcode-1

Solve problems from LeetCode.

leetcode-2

此leetcode讲解以中英双文书写，分为两部分，第一部分包含解题代码（全语言），和解题思路；第二部分包含算法总结。

leetcode-3

:pencil: Python / C++ 11 Solutions of LeetCode Questions

machine-learning

Content for Udacity's Machine Learning curriculum

ml-car-logo-classification

A simple car logo classification

ml-cnn-verification-code-recognition

This repo is about verification code recognition based on CNN. It introduced a new way to gain training dataset and recognition algorithm based on CNN.