Interview Studying
If you perchance stumbled upon this page while scrolling :), this probably seems like an awfully odd "project" to detail on one's page! But growing up, I wasn't the biggest fan of competitive math or programming (at least relative to the ecosystem I reside in), and so the style of problem-solving that is popular in technical interviews didn't (doesn't) come naturally for me. Though I aspire to do research, and likely will delve into an entrepreneurial path down the line, the skills that accompany being good at CP/math comp are still (1) tested for in research-adjacent roles and (2) generally nice skills to acquire!
- π So, I decided to gamify my side project of studying for such interviews :). This page is a semi-public way of documenting my journey and motivating myself to continue.
The plan
There are 3 main axes along which I will prepare for:
- Software engineering (LeetCode)
- Quantitative finance (Probability, Green Book, etc.)
- Machine learning (reimplementing key papers, kernel exercises, etc.)
Software engineering
LeetCode
I was recommended the NeetCode Blind 75 by a friend, so we'll start here! https://neetcode.io/practice/practice/blind75
- Arrays & hashing
- Two pointers
- Sliding window
- Stack
- Binary search
- Linked list
- Trees
- Heap / priority queue
- Backtracking
- Tries
- Graphs
- Advanced graphs
- 1D dynamic programming
- 2D dynamic programming
- Greedy
- Intervals
- Math & geometry
- Bit manipulation
What I will document
https://www.reddit.com/r/deeplearning/comments/1qnsqml/deepmind_research_scientist_interview_prep_advice/ https://gordicaleksa.medium.com/how-i-got-a-job-at-deepmind-as-a-research-engineer-without-a-machine-learning-degree-1a45f2a781de