A convolutional neural network is contrasted to the traditional neural network that uses only dense layers. This dense layer accepts inputs from all units of the previous layer. On the other hand, A convolutional layer accepts inputs from a subset of units in the previous layer.
The introduction of convolutional layers in neural networks offers several advantages over traditional neural networks that only use dense layers.
Firstly, convolutional layers can significantly reduce the number of parameters in the network, making them more computationally efficient and less prone to overfitting. Convolutional layers don't need to create weights for all inputs from the units of the previous layer, but they create only weights for the subsets of the units.
Secondly, by only accepting input from a subset of units in the previous layer, convolutional layers can capture local patterns in the data, such as edges, corners, and other features that are important for image and signal processing tasks.
Finally, convolutional layers can be stacked to learn increasingly complex and abstract features, allowing the network to recognize more complex patterns in the data. Overall, the use of convolutional layers is a key factor in the success of deep learning for image and signal processing tasks.

A convolutional neural network is contrasted to the traditional neural network that uses only dense layers. This dense layer accepts inputs from all units of the previous layer. On the other hand, A convolutional layer accepts inputs from a subset of units in the previous layer.

The introduction of convolutional layers in neural networks offers several advantages over traditional neural networks that only use dense layers.

Firstly, convolutional layers can significantly reduce the number of parameters in the network, making them more computationally efficient and less prone to overfitting. Convolutional layers don't need to create weights for all inputs from the units of the previous layer, but they create only weights for the subsets of the units.

Secondly, by only accepting input from a subset of units in the previous layer, convolutional layers can capture local patterns in the data, such as edges, corners, and other features that are important for image and signal processing tasks.

Finally, convolutional layers can be stacked to learn increasingly complex and abstract features, allowing the network to recognize more complex patterns in the data. Overall, the use of convolutional layers is a key factor in the success of deep learning for image and signal processing tasks.

Convolutional Neural Network