Predicting House Prices with Linear Regression

Explore the classic regression algorithm by predicting house prices based on features like size, location, and year built. This project gives you hands-on experience in cleaning data, training models, and interpreting results.

A Visual Guide to Learning Rate Schedulers in PyTorch

Learning rate schedulers are key to optimizing deep learning models — but choosing and tuning them can be confusing. This blog uses beautiful visualizations to explain popular schedulers like StepLR, ExponentialLR, and CosineAnnealingLR, showing how each one affects training curves.

Under the Hood: Autograd in PyTorch Explained

Autograd is the backbone of automatic differentiation in PyTorch — but how does it actually work? This blog breaks down the autograd engine, computation graphs, and how gradients flow in PyTorch operations, with code to visualize each step.