A set of blogs I wrote for PyImageSearch, a reputed Computer-Vision and Deep-Learning educational technology company.

Introduction

I began writing for PyImageSearch recently through their External Authors program, which I am a proud member of. I enjoy writing for them due to their commitment to high-quality posts and detailed walkthroughs, which are normally rare to find. Co-incidentally, I made use of this website myself more times than I can count, while I was beginning my journey into Machine Learning and Computer Vision. I was invited to do this by Aritra Roy Gosthipaty to whom I remain indebted.

Blog Posts

These blog posts are citable. Head over to the Publication section to learn more.

Getting Started with Docker for Machine Learning

This was the first part in a 3-part series which covered all the basics and essentials a Machine Learning Engineer would need to get started with Docker. It mainly introduced the concept of Containerization and the installation process of Docker

Getting Used to Docker for Machine Learning

This was the second part of the 3-part series. It introduced the most common Docker commands, how they work, and their nuances towards using them in a containerized system.

Setting Up a GPU Development Environment Using Docker

This was the final part of the 3-part series. It took up an end-to-end use-case of deploying a GPU-enabled NVIDIA PyTorch container for use on Google Cloud Platform from scratch and making use of it through a Jupyter Notebook instance.

Unlocking Image Clarity: A Comprehensive Guide to Super-Resolution Techniques

This was the first part of a 2-part series on how one can perform super-resolution using traditional and Deep-Learning based Computer Vision techniques.

Sharpen Your Vision: Super-Resolution of CCTV Images Using Hugging Face Diffusers

This was the final part of the 2-part series on Super-resolution, that included a tutorial on using Diffusers for performing super-resolution on a sample subset of CCTV images as a complete end-to-end example.