What is Neural Networks?

Types of Neural Networks and Definition of Neural Network

This blog is custom-tailored to aid your understanding of different types of commonly used neural networks, how they work, and their industry applications. The blog commences with a brief introduction to the working of neural networks. We have tried to keep it very simple yet effective.

Types of neural networks models are listed below:

The nine types of neural networks are:

• Perceptron
• Feed Forward Neural Network
• Multilayer Perceptron
• Convolutional Neural Network
• Radial Basis Functional Neural Network
• Recurrent Neural Network
• LSTM – Long Short-Term Memory
• Sequence to Sequence Models
• Modular Neural Network

An Introduction to Artificial Neural Network

Neural networks represent deep learning using artificial intelligence. Certain application scenarios are too heavy or out of scope for traditional machine learning algorithms to handle. As they are commonly known, Neural Network pitches in such scenarios and fills the gap. Also, enrol in the neural networks and deep learning course and enhance your skills today.

Artificial neural networks are inspired by the biological neurons within the human body which activate under certain circumstances resulting in a related action performed by the body in response. Artificial neural nets consist of various layers of interconnected artificial neurons powered by activation functions that help in switching them ON/OFF. Like traditional machine algorithms, here too, there are certain values that neural nets learn in the training phase.