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Types of Neural Network (TBU)

25 Feb 2019 Tags:
Neural Network, RNN, CNN


Intro

Neural Networks used in Deep Learning were inspired by the biological neural networks that form the brains of animals.

Although the concept was developed in the 1940s, the infrastructure and computing powers that enabled development of deep learning and studies of Artificial Intelligence appeared much later.

With the explosive enhancement of computing power and data storage capacities, Deep Learning with Neural Network started to prove its performance. Since then, Neural Network, A.I, Deep Learning became the buzz words.

Types of Neural Networks

There are numerous types of Neural Networks depending on its purpose and complexity of problems to solve. The diagram below summarizes almost complete chart of Neural Network structures that are being used and studied. In this post, I will only cover some of the most widely used or known structures: __Feedforward Network__, __Recurrent Neural Network__, __Convolutional Neural Network__.


1. Feedforward Neural Network

Also known as Multi-layer Perceptrons, this is the simplest form of Artificial Neural Network, where all information are processed in one direction. There are input layers, one hidden layer and output layers. There are no back loops that changes the flow.

Recurrent Neural Network (RNN)

Convolutional Neural Network (CNN)

References

  • https://towardsdatascience.com/the-mostly-complete-chart-of-neural-networks-explained-3fb6f2367464
  • https://www.learnopencv.com/understanding-feedforward-neural-networks/

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