Neural networks are algorithms that have a unique ability to extract meaningful information from complex data — Data that are extremely complex for a human brain to follow.

Let’s say a cat classifier, What are the features you would use to train a model to classify whether a given image is a cat or not? At first, it sounds easy, you would go for features like the size, color, paws, teeth, etc. But there are 40–70 different breeds of cats in the world and each of them differ somewhat in their color, size, etc. Now all of a sudden this became a tedious process. You can’t just manually find unique features in every single breed of cat. It would become a nightmare!

So, is there a way to extract the features from the input without doing any manual process? Absolutely!

The biggest advantage of Deep Learning is that we do not need to manually extract features from the image. The network learns to extract features while training. You just feed the image to the network (pixel values). What you need is to define the Neural Network architecture and a labeled dataset.

How do we “architect” a neural network?