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The feedforward pattern is a type of neural network architecture that is commonly used in machine learning and artificial intelligence applications. In this architecture, every node in a layer receives only input from descending layers, meaning that the information can only flow forward from lower layers to higher layers, thereby the name feedforward pattern.

Unlike recurrent neural networks, which use feedback connections to maintain internal state and temporal dynamics, feedforward patterns do not store information about previous inputs and therefore cannot learn sequential patterns. However, they are well-suited for applications where patterns do not have a temporal component, such as classification problems and regression problems.

Feedforward patterns typically consist of multiple layers of interconnected nodes or "neurons". The first layer is the input layer, which presents the input data to the network. Each subsequent layer applies a linear transformation to the input, followed by an activation function. The final layer, known as the output layer, generates the final prediction or classification.

A characteristic of feedforward patterns is the use of multiple layers to increase representation capacity and to learn increasingly abstract features from the raw data. By stacking multiple layers of such linear and nonlinear transformations, these networks can learn to recognize complex patterns in the data and make accurate predictions.

The feedforward pattern is widely used in various fields, including speech recognition, image classification, and natural language processing. Its design makes it a simple and effective network architecture that can be scaled to large amounts of data, making it a popular choice for many applications.