With the potential to bridge the gap between the capabilities of humans and machines, Convolutional Neural Networks  CNN stands superior powering the technology of computer vision.

Holding a higher ranking in the field of Machine learning, CNN is based on a mathematical operation of convolution that is applied to a matrix and allows the merger of two sets of information. It uses filters to extract features from images to reduce the processing requirements without losing the features valuable for accurate prediction. CNN comprises an input layer, an output layer, and hidden layers of multiple convolutional layers, pooling layers, and fully connected layers. The core building block of the CNN is the convolutional layer that is responsible for recognizing features in pixels, the pooling layers make these features more abstract and the fully connected layers use the acquired features for prediction.

By capturing the spatial features of an image, CNN is dominating computer vision with its applications like facial recognition, Optical character recognition, visual search, image classification, and driverless cars, to name a few.

Bringing in a world of difference with its superior performance with image, speech, or audio signal inputs, can the same image in different angles, different backgrounds, and different lighting conditions make CNN go off the air?

- MADHAVI DESAI