Convolutional Neural Network (CNN)
Convolutional Neural Networks (CNNs or ConvNets) are a specialized kind of neural networks used extensively in the field of deep learning, particularly for tasks involving image and video recognition, image classification, medical image analysis, and natural language processing, among others. CNNs are designed to automatically and adaptively learn spatial hierarchies of features from input images.
The architecture of a CNN typically consists of a series of layers, including convolutional layers, pooling layers, and fully connected layers. Convolutional layers apply a convolution operation to the input, passing the result to the next layer. This process allows the network to focus on local input patterns and maintain spatial relationships between parts of the image.
Pooling layers reduce the dimensions of the data by combining the outputs of neuron clusters at one layer into a single neuron in the next layer. CNNs are known for their ability to capture the temporal and spatial dependencies in an image through the application of relevant filters, making them highly efficient in tasks like detecting faces, objects, and traffic signs, among others.
An example of a CNN application is in facial recognition technology, where the network learns to identify and distinguish facial features and structures by analyzing thousands of labeled images of faces. Through training, the CNN learns to recognize patterns and features that are characteristic of particular faces and can then identify these features in new images.
Another example is in medical imaging, where CNNs are used to detect anomalies such as tumors in MRI scans or X-rays. By learning from a large set of labeled medical images, a CNN can highlight areas of interest and assist radiologists in diagnosing diseases more accurately and efficiently.