Principal Component Analysis for Image Dataset Compression — In my previous article, we took a graphical approach to understand how Principal Component Analysis works and how it can be used for data compression. If you are new to this concept, I strongly suggest you read my previous article before proceeding. I have provided the link below: Principal Component Analysis — Visualized Data compression using Principal Component Analysis (PCA)towardsdatascience.com

Data compression using Principal Component Analysis (PCA) — 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…

A visual approach to understanding machine learning — 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…

Monitoring your neural network’s training made easy — Logging the loss and accuracy curves has never been an easy job. We often save these values in arrays or lists and then plot them at the end of the training. Sharing these graphs is even harder where sending screenshots of these graphs seems the only choice. In this tutorial…

Can computers really understand the human face? — 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. Colab Notebook The complete code can be found in the interactive Colab Notebook below. Face Landmark Detection colab.research.google.com