Hello World ✌🏻
I am Ayush. I am a student, a teacher, and a practitioner of data science.
Here I blog/jot/dump/scatter ideas/muses/learnings/experiences on all things data and software.
I am based in Nepal 🇳🇵 and I currently work as a Staff Data Scientist 📈👨💻 at Cloudfactory. I recently graduated from Georgia Institute of Technology 🐝 Analytics program (focusing on Computational Data Analytics and Machine Learning). I am also building a data community in Nepal via Code for Nepal. When I am AFK, I am lifting weights.
Posts
An extensive guide to Discrete Event Simulation (DES), covering mathematical foundations, practical applications, and SimPy implementation.
A primer at sampling methodologies, including probability and non-probability sampling methods, sample size determination, and minimizing bias.
I spoke at TEDx (DWIT College) on 'Nepal in the Loop'. The talk focused on the remarkable role Nepal is playing—and can play—in the global data landscape.
The paper introduces a novel architecture called residual networks (ResNets), which significantly improves deep neural network training by using skip connections to mitigate the vanishing gradient problem. This approach achieved state-of-the-art performance on several benchmarks, including the ImageNet dataset, and has become foundational in modern deep learning applications.
A machine learning paradigm where a model is trained on certain tasks and then applied to new, unseen tasks without additional training. It leverages generalizable knowledge to perform well on tasks it has not explicitly encountered during training. (an instance og transfer learning)
From optimization, to convex optimization, to first order optimization, to gradient descent, to accelerated gradient descent, to AdaGrad, to Adam.