Beyond 3D: Tensors, 100 Dimensions and an SVM From Scratch

Sat, 08 Aug 2026 13:28:00 +0100

PyTorch: Zero to Hero — Advanced Interlude 06A

There is a point in machine learning where the numbers become difficult for a human being to picture.

One dimension is easy.

Two dimensions are easy.

Three dimensions are still possible to draw.

Then somebody hands you a tensor containing 100 features.

Or 768.

Or 4,096.

And suddenly the language changes.

People start talking about embedding spaces, feature directions, hyperplanes, high-dimensional geometry and latent representations.