Here is a set of notes maintained by me and some others on concepts related to event-sriven reactive programming in Scala: https://github.com/sjuvekar/reactive-programming-scala/blob/master/ReactiveCheatSheet.md. The notes originated from a Coursera's class with same title: https://class.coursera.org/reactive-001/class
Kaggle just concluded their Personalized Ranking Competition hosted by Expedia. Here is a link to the competition This competition is a standout because the dataset provided is considerably bigger than their earlier competitions (about 10M X 30) and the target classes are unbalanced with about 90% of provided data consisting of single class. Any straightforward learning algorithm has many issues:
Sorry for being inactive for a month. Was busy with some family matters. Here I am again, with, well not much to add except this paper . This title of the paper is the title of this post. The paper talks about automatically adapting learning rate in Stochastic Gradient Descent (SGD) using curvature of error surface. Enjoy!
Kaggle's CauseEffect Pair Competition has ended and I got a rank of 19 out of 269 teams --- my second top 10% finish. Here is a link to the competition http://www.kaggle.com/c/cause-effect-pairs. Here is a brief description of the contest: You are given a large number of A-B observation pairs, each pair itself contains a list (a_i, b_i). Each pair has an indicator assigned to it A->B (meaning A is a cause of B), B->A (meaning B is a cause of A) and A-B (meaning neither, they could be independent or could be affected by a third common cause etc). The aim of the contest was to predict similar indications - A->B, B->A, A-B on new unknown pairs. The competition involved a lot of feature engineering. The learning part was mostly handled by off-the-shelf learning algorithm --- I used Gradient Boosting Forest. But the one with better feature was the winner. (For example, one of the winners developed thousands of features out of the pair). Here is a discussion of everyone's approaches: http://www.kaggle.com/c/cause-effect-pairs/forums/t/5643/sharing-methods. Finally, here is a comprehensive list of what worked and what not: http://clopinet.com/causality/FactSheetv1.pdf , compiled by the hosts.
Passport.js is a beautiful authentication middleware for node.js, but getting it right is hard for the first time. I am going to point to my own repo: https://github.com/sjuvekar/3Dthon/tree/master/auth to refer to how I did it the last time. Basic stages are simple:
Here's a link to passport js just in case.
- Include appropriate strategies (local/Facebook/Google oauth etc)
- Define a callback function under each strategy to create appropriate mongoDB user object, insert it into database or flash an appropriate error msg ( connect-flash is useful here) and redirect to appropriate route.
- Finally, define appropriate route like /auth/google/callback and call passport.authenticate("google") etc inside it.
Creating social logins with passport.js is super-easy. Remember three steps:
Here are some of the resources for selecting attractive fonts/icons and designs for bootstrapping for your website
This post is all about multiple online tools to create wireframing for a new website and start a new business. Most of the references are from an excellent class on Coursera on Startup Engineering. I have listed them here for a quick reference. Most of the design principles today rest on responsive design, a webpage that floats and re-adjusts itself based on the device and screen-size. Writing pixel-perfect and custom CSS for such a design in a time-consuming task.