👉 Rounds computing is a method used in machine learning, particularly in deep learning, to handle the complexity of high-dimensional data by breaking it down into multiple stages or "rounds." Each round involves passing the data through a series of layers, where each layer applies a transformation to the input data using a set of learnable parameters, such as weights and biases. These layers can include convolutional layers for image data, recurrent layers for sequential data, or fully connected layers for general-purpose tasks. By iterating through these rounds, the model progressively extracts more abstract and meaningful features from the input data, allowing it to learn complex patterns and relationships. The number of rounds can vary depending on the problem and the architecture, but it generally helps in capturing intricate details and improving the model's performance on tasks like image recognition, natural language processing, and more.
