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Artificial Neural Network Versus Convolutional Neural Network

Neural networks are incredibly advanced and one of the most important technologies ever developed. They form the building blocks of artificial intelligence and machine language. Apple’s Siri, Google’s Voice and Lens, and facial recognition systems are all based on neural networks.

What Is an Artificial Neural Network?

An artificial neural network (ANN) is a machine learning approach that draws inspiration from how neurons signal each other in the human brain. ANNs are built from interconnected nodes or neurons in a layered structure mimicking a human brain. 

ANNs learn from data (or learning algorithms) and can be trained to recognize patterns, classify data, and forecast future events. They are sometimes referred to as feed-forward neural networks as inputs are processed only in the forward direction. Because the nodes are interconnected, ANNs store information on the entire network.

What Is a Convolutional Neural Network?

Convolutional Neural Networks (CNNs) are some of the most popular neural network models used today. CNNs are designed with multilayer perceptrons that contain at least one convolutional layer that can be entirely connected or pooled. They process data with a grid-like topology instead of in a unidirectional data flow. 

CNNs are capable of learning directly from data, eliminating the need for manual feature extraction. As a result, they are very accurate when doing image recognition. They are popularly used to find patterns in images to recognize objects, faces, and scenes, as well as for image classification and for object and face detection. The advancement of neural networks has also made it possible to turn photos into painted art and painting style transfer with AI.

They accomplish these processes by creating feature maps that record sections of the image, designating weights based on the different objects of the image, and then distinguishing the images from each other. A key feature behind the success of CNNs is their ability to automatically detect important features without any human supervision.

Differences Between ANNs and CNNs

One of the basic differences between the two neural network models is that ANNs operate on tabular or text data, while CNNs operate on image data. The main distinction between a CMNN and a typical ANN, however, is that only the final layer of a CNN is fully connected, whereas every neuron in an ANN is connected to every other neuron.

ANNs are the simplest forms of neural networks because they pass information in one direction, whereas CNNs use a variation of multi-layer perceptrons capable of passing information in different directions. The interconnectedness of CNNs creates a more powerful model that requires more processing power than ANNs that deal primarily with text data.

Because they are feed-forward neural networks, ANNs do not support parameter sharing, while interconnected perceptrons make parameter sharing in CNNs possible. ANNs can also work with an incomplete data set or incomplete knowledge. On the other hand, a lot of training data is required for CNNs to work accurately. Their grid-like topology means spatial relationships (the orientation of data or objects possible) is possible in CNNs, while ANNs lack this feature.

Similarities Between ANNs and CNNs

Although they are different neural network models, ANNs and CNNs do share some common traits. Neither model supports recurrent connections–this is a feature of recurrent neural networks (RNNs), which is evident when you compare convolutional and recurrent neural networks.

ANNs and CNNs operate on fixed-length input (all the records are exactly the same length), and both models use error measures to improve their learning processes and produce epochs to analyse the effectiveness of the models generated.

In Conclusion

Artificial neural networks and convolutional neural networks are two of the most popular models of neural networks; they each have different features that appeal to different needs. While ANNs have more fault tolerance and can work with incomplete knowledge, CNNs have high accuracy when issued for image recognition and feature-weight tasks.

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