Deep Learning Autoencoders

Interested in knowing how retailers like Amazon gives you recommendations. Like customer who bought this item also bought or how Netflix recommends movies, then read on…

All this can be achieved using unsupervised deep learning algorithm called Autoencoder.

It always helps to relate a complex concept with something known for easy understanding. Let’s try to relate Autoencoders to something we know.

What is Autoencoder?

We buy a service or an item on the internet. we ensure that the site is secure by checking they use https protocol. We enter our credit card details for the purchase. Our credit card details are encoded over the network using some encoding algorithm. Encoded credit card detail is decoded to generate the original credit card number for validation.

In our credit card example, we took the credit card details, encoded it using some function. Later decoded it using another function to reproduce the output identical to the input. This is how autoencoders work.

Autoencoders encodes the input values x, using a function f. It then decodes the encoded values f(x), using a function g, to create output values identical to the input values.

Autoencoder ‘s objective is to minimize reconstruction error between the input and output. This helps autoencoders to learn important features present in the data. When a representation allows a good reconstruction of its input, then it has retained much of the information present in the input.

How does Autoencoders work?

We take the input, encode it to identify latent feature representation. Decode the latent feature representation to recreate the input. We calculate the loss by comparing the input and output. To reduce the reconstruction error we back propagate and update the weights. Weight is updated based on how much they are responsible for the error.

Let’s break it down step by step.

In our example, we have taken the dataset for products bought by customers