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A neural network that contains at least one layer is known as a convolutional layer. A convolutional neural network would generally consist of some combination of the below mentioned layers:

- Convolutional layers
- Pooling layers
- Dense layers

Convolutional Neural Networks have been used to produce great results for a specific kind of problems, such as image recognition.

It is a Deep Learning algorithm that takes an image as input, assigns importance to it, i.e. the algorithm learns to assign weights and biases to values. This helps differentiate one object from the other.

The amount of pre-processing required in a ConvNet is lesser than other classification algorithms. In some situations, the filters are hand-engineered, but with enough training, convolutional network (also known as ConvNets) can learn these filters/characteristics.

The architecture of a ConvNet is similar to the connectivity pattern present in neurons of the human brain.

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