This article is an implementation of a research paper titled “Shortest Path Distance Approximation using Deep Learning Techniques”, where the authors explain a new method to approximate the shortest path distance between the nodes of a graph. I will explain the paper and my implementation of it. You can find the project on my GitHub account here. First I will give an overview of the method proposed in this paper, then we will go through some of the concepts used in this paper to solve the problem and finally the implementation.
— Read on towardsdatascience.com/shortest-path-distance-with-deep-learning-311e19d97569
In my experience, ML projects come in all shapes and sizes and vary greatly in their complexity. In the initial 2000/10s, the emphasis was on the model-centric approach which I always found a little bizarre as I heard umpteen times about the fancier models than the results(well, it was a different time and folks used to get awestruck when they would hear ML, AI, DS etc); slowly and steadily the focus has shifted to the result centric approach i.e. use whatever model you can but make use of the data and produce results that are directional, applicable, and coherent.
— Read on towardsdatascience.com/running-machine-learning-projects-things-to-know-316775338eab