Classification of Neural Networks
There are various types of neural networks. Neural networks can be classified based on their structure, the type of data they are designed to handle, and the learning algorithms they use. In this tutorial, we’ll learn about some main types of neural networks.
Artificial Neural Network (ANN):
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It is also known as Feedforward Neural Networks.
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ANN is one of the basic type of neural network architecture. In this, multiple layers of interconnected nodes are used to process input data.
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Artificial neural network commonly include an input layer, one or more hidden layers, and an output layer.
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In ANN each node in a layer takes a weighted sum of inputs, applies an activation function, and passes the result to the next layer.
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ANN is used for tasks such as image classification, regression, and language modeling.
Convolutional Neural Network (CNN):
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CNN is a neural network architecture that is specifically designed for image processing tasks.
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It commonly include series of convolutional layers, pooling layers, and fully connected layers.
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Convolutional layers apply filters to the input image to extract features, while pooling layers downsample the feature maps to reduce their dimensionality. Then it comes to fully connected layers, which process the output of the convolutional layers to make a prediction.
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CNNs are used for tasks such as image classification, object detection, and image segmentation.
Recurrent Neural Network (RNN):
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RNN is another main type of neural network architecture that is used for sequential data processing tasks like - speech recognition, machine translation, and text generation.
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RNNs have loops in their architecture that allow information to be passed from one step to the next step.
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This makes them well suited or more useful for tasks where the input data has a sequential structure.
