Python

Introduction Ever wondered how Instagram applies stunning filters to your face? The software detects key points on your face and projects a mask on top. This tutorial will guide you on how to build one such software using Pytorch. Dataset In this tutorial, we will use the official DLib Dataset which contains 6666 images of varying dimensions. Additionally, labels_ibug_300W_train.xml (comes with the dataset) contains the coordinates of 68 landmarks for each face....

Introduction to machine learning In the traditional hard-coded approach, we program a computer to perform a certain task. We tell it exactly what to do when it receives a certain input. In mathematical terms, this is like saying that we write the f(x) such that when users feed the input x into f(x), it gives the correct output y. In machine learning, however, we have a large set of inputs x and corresponding outputs y but not the function f(x)....

Introduction In this article, we will learn how PCA can be used to compress a real-life dataset. We will be working with Labelled Faces in the Wild (LFW), a large scale dataset consisting of 13233 human-face grayscale images, each having a dimension of 64x64. It means that the data for each face is 4096 dimensional (there are 64x64 = 4096 unique values to be stored for each face). We will reduce this dimension requirement, using PCA, to just a few hundred dimensions!...

Introduction If you have ever taken an online course on Machine Learning, you must have come across Principal Component Analysis for dimensionality reduction, or in simple terms, for compression of data. Guess what, I had taken such courses too but I never really understood the graphical significance of PCA because all I saw was matrices and equations. It took me quite a lot of time to understand this concept from various sources....

Description Developed a multi-stage LLM pipeline using Llama3 to extract key information from X-Ray report images, leading to the creation of a new product in our suite. Architected and developed a cloud-native version of the product on Kubernetes and packaged it as a Helm chart for ease of deployment, distribution and versioning. Enhanced operational efficiency by implementing GitOps at scale using ArgoCD, enabling centralized management of multiple K8s clusters, cutting down the release and software update timings by a magnitude....