Introduction to Deep Learning
Convolutional Neural Networks (CNNs) are a type of neural network that are commonly used in computer vision tasks, such as image classification and object recognition. They are designed to process data with a grid-like topology, where neighboring data points are highly correlated. This makes them especially effective at tasks where spatial relationships between data points are important.
At a high level, a CNN consists of a series of convolutional layers, each of which applies a set of filters to the input data. The filters are learned automatically during training, and are designed to identify specific features in the input data. For example, in an image classification task, the filters might learn to identify edges, corners, and other simple shapes. These features are then combined in subsequent layers to form higher-level representations of the input data.
One of the key advantages of CNNs is their ability to learn spatial hierarchies of features. Each layer of the network can learn increasingly complex representations of the input data, by combining features learned in previous layers. This makes them highly effective at tasks such as object recognition, where the network must identify objects based on their constituent parts.
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