Description: A Modular Neural Network is a neural network architecture composed of multiple modules, each designed to address specific tasks within a broader system. This modular structure allows each component to specialize in a particular aspect of the problem, enhancing the overall efficiency and accuracy of the model. Modular neural networks are especially useful in situations requiring a multifaceted approach, as they can be combined to solve complex problems more effectively than a monolithic neural network. Each module can be trained independently, facilitating adaptation and continuous improvement of the system. Furthermore, this architecture allows for greater flexibility, as modules can be added or removed as needed, making it an attractive option for applications in fields such as computer vision, natural language processing, and robotics. In summary, modular neural networks represent a significant advancement in how neural networks are designed and utilized, allowing for greater specialization and efficiency in solving a wide range of complex problems.
