We test the efficiency of applying Geometric Deep Learning to the problems in low-dimensional topology in a certain simple setting. Specifically, we consider the class of 3-manifolds described by plumbing graphs and use Graph Neural Networks (GNN) for the problem of deciding whether a pair of graphs give homeomorphic 3-manifolds. We use supervised learning to train a GNN that provides the answer to such a question with high accuracy. Moreover, we consider reinforcement learning by a GNN to find a sequence of Neumann moves that relates the pair of graphs if the answer is positive. The setting can be understood as a toy model of the problem of deciding whether a pair of Kirby diagrams give diffeomorphic 3- or 4-manifolds.

In many ML interview rounds, candidates are asked to demonstrate their coding skills w.r.t. These kind of rounds helps to identify both coding skills as well as ML skills required to be a top notch ML engineer in some of the top companies. Let's look at some of the common ML coding problems asked in such interviews: Given the API rand7() that generates a uniform random integer in the range [1, 7], write a function rand10() that generates a uniform random integer in the range [1, 10]. You can only call the API rand7(), and you shouldn't call any other API. Please do not use a language's built-in random API.

As I have been writing games in Python to improve my programming skills, I have written a short game concerning random numbers. The player writes down a number between 0 and 100, and the computer gets to guess what the number is. This guessing game is composed as a while loop and an if statement within the loop. A flowchart of the program is displayed below to illustrate the complex nature of the loops and conditional statements that I coded. The flowchart above is pretty self explanatory, but the code, listed below states how the program works.