👉 The descending project, often associated with the concept of "deep learning," refers to a series of neural network architectures that progressively reduce the dimensionality or complexity of data representations as they move from higher layers to lower layers. This process typically starts with a large, detailed representation of the input data in the initial layers and gradually simplifies it, capturing more abstract and generalized features in subsequent layers. This hierarchical reduction is crucial for tasks like image recognition, natural language processing, and other complex data analysis, as it allows models to learn increasingly sophisticated patterns and features from raw data.
