Blog

I regularly write about topics in Machine Learning and Artificial Intelligence. Some of these posts are my personal reference notes, which, I hope would also be useful to others. Whenever applicable I also provide code in the form of jupyter notebooks. To receive regular updates follow me on Medium.

How to Fix Poor Culture at Work: Six Must-Read Books for Engineers
Published: 20.03.2025

Six Ways to Control Style and Content in Diffusion Models
Published: 11.02.2025

Six Books That Changed My Attitude to Managing Engineering Teams
Published: 15.01.2025

LLM Routing — The Heart of Any Practical AI Chatbot Application
Published: 15.01.2025

How to grow your career without feeling stuck
Published: 17.04.2024

Face Off: Practical Face-Swapping with Machine Learning
Published: 03.04.2024

Three challenges in deploying generative models in production
Published: 07.08.2023

20 Learnings From Delivering Cross-Functional Machine Learning Projects
Published: 07.08.2023

Become An Effective Machine Learning Team Lead
Published: 06.02.2023

What is Variable Rate Shading and Why Big Players are Rushing to Get It
Published: 05.10.2022

On the edge — deploying deep applications on mobile
Published: 31.07.2022

Acing Machine Learning Interviews
Published: 03.06.2022

Even Google does not get it right
Published: 24.05.2022

Deep Video Inpainting
Published: 23.03.2022

Three Soft Skills Every PhD Student Gets for Free
Published: 10.02.2022

Seven Questions to Ask before Introducing AI into Your Project
Published: 05.11.2021

Beyond correlation coefficients and mean squared error
Published: 09.09.2021

Deep lifelong learning — drawing inspiration from the human brain
Published: 02.09.2021

Nine Tools I Wish I Mastered before My PhD in Machine Learning
Published: 26.08.2021

Perceptual Losses for Deep Image Restoration
Published: 08.05.2021

Scientific computing — lessons learned the hard way
Published: 15.04.2021

Deep Image Quality Assessment
Published: 15.03.2021

Active sampling for pairwise comparisons
Published: 08.02.2021

Hyper-parameter tuning with Bayesian optimization or how I carved boats from wood
Published: 12.01.2021

Convolutional neural networks — the essential summary
Published: 10.10.2020

Dataset fusion for large-scale preference aggregation
Published: 03.10.2019

Sampling distributions with an emphasis on Gibbs sampling, practicals and code
Published: 22.04.2019

Confidence intervals: parametric and non-parametric resampling
Published: 26.11.2018

Unsupervised deep learning for data interpolation
Published: 06.10.2018