Convolutional neural network (CNN)

Convolutional neural networks (CNNs), also called ConvNets, are a type of deep learning model for analyzing visual data like images and videos. They process data through a series of layers.

• The convolutional layers look for simple patterns in the input data, such as lines, edges, or shapes, and highlight where they appear in the image.
• The pooling layers shrink the processed data to keep the most important parts, making the network faster and helping it avoid memorizing the training data too closely.
• The fully connected layer combines the information from earlier layers to decide what the image represents, producing a final classification or output.

CNNs have different real-world applications. In marketing, they enable automated photo tagging on social media; in healthcare, they assist radiologists in identifying tumors. They are computationally intensive, so they often require GPUs to process large amounts of data efficiently during training and inference.

Deep learning frameworks such as TensorFlow, PyTorch, and Keras have made it easier to develop and train CNNs, helping businesses and researchers implement these models efficiently and at scale.