Hey pupil! Welcome to the next lecture on modern neural networks. I hope you are doing great. In the previous lecture, we saw the EffcientNet neural network, which is a convolutional Neural Network (CNN), and its properties. Today, we are talking about another CNN network called the capsule neural network, or CapsNets. These networks were introduced to provide the capsulation in CNNs to provide better functionalities.
In this article, we will start with the introduction of the capsule neural network. After that, we will compare these with the traditional convolutional neural networks and learn some basic applications of these networks. So, let’s start learning.
Capsule neural networks are a type of artificial neural network that was introduced to overcome the limitations of CNNs. In 2017, these modern neural networks were designed by Geoffrey Hinton and his team working in the Google AI research center.
These are some of the most popular and searched neural networks because they deal with the inefficiency of CNN in recognizing the results when the input data has different orientations. The capsule Neural networks are made by getting inspiration from the visual cortex of the human brain to process information.
The neural networks are categorized in different ways on the basis of their arrangement of layers. Usually, the neural networks have the same structure but slightly different performance and other features. However, the workings of CapsNet are far more different from those of traditional neural networks; therefore, there is a need for a detailed study of structure and performance. Here are some key features of Capsule neural networks that make them different from other traditional neural networks
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