Microsoft officially retires Internet Explorer. Web Developers:

Content If you know, you knowle Quora Search Quo Dating and Friendship Gender Friendship Advice Friendship Dating Advice Personal Question like girl A. We are very good friends. A's best friend B likes me. Also, A likes a guy C who is my best bro and C likes another girl D who is in a relationship with another guy. What should we do? " Answer a) Follow - 143 Request 000 90 Answers Priyanka Dhaka, studied at Indian Institute of Technology, Delhi Updated Jul 28, 2014 Originally Answered: I like girl A. We are very good friends, but her best friend B likes me. Also, A likes guy C, who is my best bro. C likes another girl D, who is in a relationship with another guy. What should we do? You can apply Bipartite Matching algorithm of Graph Theory. All you have to do is: 1. Put all girls as vertices (a's) in Part A, and boys as vertices (b's) in Part B. 2. Draw an edge between vertex a to vertex b if a likes b. 3. It will be a bipartite graph (there are no edges within vertices of Part A, and same for Part B) unless your friends are not straight. 4. Then find maximum matching for this bipartite graph. (refer this link Maximum Bipartite Matching - GeeksforGeeks C) 5. You will get maximum matching as output and the matched edges will be the pairs who should be together. 6. You can apply weighted bipartite matching algorithm if you know how much they lovelike each other. In that case, assign weights according to their amount of love. and repeat step 4 and 5. Thanks for asking this question. Finally I have found a real life problem which can be solved by what Prof Panda has been teaching us for 2-3 years. P.S. I can provide you code for both weighted and unweighted maximum bipartite matching which I made for my project. 203.1K views View 13.701 upvotes : View 1 share

Content Planet Deaths 120,315,672,896 Has JavaScript NO NO YES NO NO NO NO NO NO COINCIDENCE?

Content my linkedin profile R, python, javascript, shiny, dplyr, purrr, ditto, ggplot, d3, canvas, spark, sawk, pyspark, sparkly, lodash, lazy, bootstrap, jupyter, vulpix, git, flask, numpy, pandas, feebas, scikit, pm, bayes, h2o.ai, sparkling-water, tensorflow, keras, onyx, ekans, hadoop, scala, unity, metapod, gc, cllc, krebase, neoj, hadoop. I typically ask recruiters to point out which of these are pokemon.

Backend dev passes a note to Frontend dev in class. Frontend opens it to find just "500 Internal Server Error" written inside. Classic backend communication - technically accurate, completely unhelpful. The backend probably thinks they've provided all necessary information while the frontend is left wondering what the hell they're supposed to do with this. Just another day in the web development classroom of life.

Nine data professionals standing around watching while one Excel guru does all the actual work. Classic corporate data science theater. The entire AI department, with their fancy degrees and machine learning models, rendered useless by someone who mastered VLOOKUP and pivot tables. That's what happens when you spend $2 million on a data lake but can't figure out how to drain a real one.

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