What is the Working of Image Recognition and How it is Used?

Before a classification algorithm can do its magic, we need to train it by showing thousands of cat and non-cat images. The general principle in machine learning algorithms is to treat feature vectors as points in higher dimensional space. Then it tries to find planes or surfaces (contours) that separate higher dimensional space in a way that all examples from a particular class are on one side of the plane or surface. To build a predictive model we need neural networks. The neural network is a system of hardware and software similar to our brain to estimate functions that depend on the huge amount of unknown inputs.