Alan Turing

Equiangular Spiral - We grow like a spiral

SGD Introduction | Parameter Update in Neural Networks (Lecture 1)

Mini Batch SGD | Loss Curve | Parameter Update in Neural Networks (Lecture 2)

Why SGD Is So Fast (One Epoch, Thousands of Steps) Why is Stochastic Gradient Descent often faster than Batch Gradient Descent? The key difference is simple: Batch Gradient Descent computes one update after summing over all data points. SGD removes that sigma and updates the weights using just one data point at a time. That means if you have 10,000 data points, SGD updates the weights 10,000 times in a single epoch. As this animation shows, SGD often reaches near-optimal regions very quickly—som

PyTorch Adam Optimizer: Train Neural Nets Faster in Python