There are a lot of topics in the field of machine learning that can be puzzling, but two neural networks are transforming technology. They power so many of the innovative technologies that we have come across, but what are these exactly? Let’s take things one at a time and understand the concepts in a more basic way.

Neural Networks Overview

You can imagine neural networks as the computer version of how a person’s mind operates. And just as there are neurons wired in the brain, there are also nodes (sometimes referred to as neurons) in the network which work together to accomplish certain tasks. They are built to be able to grasp from experience, thus making the machines smarter than they were initially.

The Process of Neural Networks

It’s about layers. Simply stating, layers are crucial for the structure and the function of the neural network. It begins with the first image or pieces of text that gets inputted into the data network. After that, each layer processes and continues to enhance the input data so that it can deliver even better output. With each step, the probability of getting accurate results increases, allowing the network to learn more intricate patterns.

Types of Neural Networks

In the field of neural networks, there is no one type that accommodates all. Different types are applied to different purposes:

Neural Feedforward Neural Networks feed simpler networks; thorough information advances only in one direction, configuring them for the proper classification of spam detection.

But with the so-called recurrent neural networks (RNNs), we take it further. These networks are able to handle time-series data in ways that other feedforward neural networks cannot.