Visualising the world of competitive programming with Python (Codeforces)
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This document discusses competitive programming and compares several competitive programming websites. It finds that Codeforces has the most programming questions overall and hosts weekly programming contests. Codeforces also uniquely makes all participant code public and uses a rating system similar to chess ratings. While other sites like Hackerrank and Hackerearth have additional topics, Codeforces focuses solely on programming contests and exposes participants to a wide range of languages and problem types. The document shows Codeforces has the most questions in most categories like sorting and dynamic programming. It also finds Python is mostly used for simpler implementation questions while other languages may be better for more complex categories.
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Building a real system that incorporates machine learning as a part can be a difficult effort, both in terms of the algorithmic and engineering challenges involved. In this talk I will focus on the engineering side and discuss some of the practical issues we’ve encountered in developing real machine learning systems at Netflix and some of the lessons we’ve learned over time. I will describe our approach for building machine learning systems and how it comes from a desire to balance many different, and sometimes conflicting, requirements such as handling large volumes of data, choosing and adapting good algorithms, keeping recommendations fresh and accurate, remaining responsive to user actions, and also being flexible to accommodate research and experimentation. I will focus on what it takes to put machine learning into a real system that works in a feedback loop with our users and how that imposes different requirements and a different focus than doing machine learning only within a lab environment. I will address the particular software engineering challenges that we’ve faced in running our algorithms at scale in the cloud. I will also mention some simple design patterns that we’ve fond to be useful across a wide variety of machine-learned systems.
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Visualising the world of competitive programming with Python (Codeforces)
- 1. Visualising the world of Competitive Programming Anuj Menta
- 2. –<random_senior> “Learn something other than Python. It won’t fetch you a job”
- 3. • more commonly known as algorithmic programming. • Popular among Computer Science graduates across all universities (:P) • Optimizing different operations using complex data structures and algorithms like Binary Trees, Dynamic Programming etc • Common websites to practice: Codechef, Hackerrank, Codeforces, Hackerearth, SPOJ etc.
- 4. Then why Codeforces? • Only has programming contests hosted on a weekly basis. • The most important thing : Every participants code is public • A unique user rating system(A bit similar to the elo rating system) • Very diverse in terms of programming languages and types of problems.
- 5. –Anuj Menta “All the numbers depicted might have a confidence interval of about 90%. The study does not mean to undermine or promote any website”
- 6. Number of questions Just to start off with a small comparison 0 1000 2000 3000 4000 Codeforces Hackerrank Codechef Hackerearth
- 7. Hackerrank and Hackerearth have problem sets on other topics like Artificial Intelligence, Math etc (+10 points :P)
- 8. Number of questions | Category-wise Categories: Sorting - Dynamic Programming 0 1 Codeforces Hackerrank Codechef Hackerearth
- 9. Number of submissions Total number of submissions made for existing problem sets 0 1 Codeforces Hackerrank Codechef Hackerearth
- 11. Comparison of Programming Languages A comparison on all the four websites on 10 languages 0 1
- 12. –Some C++ stud “Competitive programming is independent of programming language”
- 13. But still.. How far can you possibly go if you only know Python?
- 14. Broad Categories of Questions • Simple Implementation • Sorting • Search • Trees • Linked Lists/Heaps/Queues/Stacks/Heaps • Graph Theory • Greedy algorithms • Dynamic Programming • Recursion • NP Complete • Advanced Data Structures
- 15. Proportion of Python submissions The percentage of python submissions for different categories of questions 0 0.25 0.5 0.75 1 Implementation Sorting Search Trees
- 16. Comparisons to be added: • Performance : Python vs. others • Computation time: Python vs. others • Number of chars/lines of code : Python vs. others • Programming style : High rated coders vs. Low rated coders • Users churn rate( classified by programming language)