CNN (Convolutional Neural Network)

Information

• CNN (Convolutional Neural Network) is a type of neural network that has been widely used for image and video processing tasks.
• CNNs consist of multiple layers that extract and transform features from input data.
• The core building block of a CNN is the convolutional layer, which applies a set of learnable filters to the input data.
• The filters convolve across the input data, computing dot products and producing a set of output values that represent the presence of particular features in the input data.
• The output of the convolutional layer is then passed through a non-linear activation function to introduce non-linearity to the model.
• Pooling layers are often used in CNNs to reduce the dimensionality of the output of the convolutional layer, by taking the maximum or average value of a subset of the output values.
• CNNs typically end with one or more fully connected layers, which combine the extracted features and produce a prediction or classification for the input data.
• One of the key advantages of CNNs is their ability to automatically learn and extract features from raw input data, reducing the need for feature engineering.
• CNNs have been used for a wide range of applications, including image classification, object detection, and natural language processing.