Main Types of Neural Networks and its Applications — Tutorial
Main Types of Neural Networks and its Applications — Tutorial

A tutorial on the main types of neural networks and their applications to real-world challenges.

Author(s): Pratik Shukla, Roberto Iriondo

Last updated, August 11, 2020

Nowadays, there are many types of neural networks in deep learning which are used for different purposes. In this article, we will go through the most used topologies in neural networks, briefly introduce how they work, along with some of their applications to real-world challenges.

Figure 2: The perceptron: a probabilistic model for information storage and organization in the brain [3] | Source: Frank Rosenblat’s Mark I Perceptron at the Cornell Aeronautical Laboratory. Buffalo, New York, 1960 [4]

📚 This article is our third tutorial on neural networks, to start with our first one, check out neural networks from scratch with Python code and math in detail. 📚

Neural Network Topologies

Figure 3: Representation of the perceptron (p).

1. Perceptron (P):

The perceptron model is also known as a single-layer neural network. This neural net contains only two layers:

Input Layer

Output Layer

In this type of neural network, there are no hidden layers. It takes an input and calculates the weighted input for each node. Afterward, it uses an activation function (mostly a sigmoid function) for classification purposes.

Applications:
['Towards Ai Team']
['Artificial Intelligence', 'Education', 'Innovation', 'Science', 'Technology']