Learning

ImageNet is more than just a dataset. The sheer scale of ImageNet, combined with its detailed labeling, made it essentially the backbone of Computer Vision.

A neural network is like a highly sophisticated, multi-layered calculator that learns from data. It consists of numerous “neurons” (tiny calculators) connected in layers, with each layer performing a unique function to help the network make predictions or decisions.

The beauty of VAEs lies in their ability to generate new samples by randomly sampling vectors from this known region and then passing them through the generator part of our model.

An autoencoder begins its journey by compressing input data into a lower dimension. It then endeavors to reconstruct the original input from this compressed representation.