Description: Modular Neural Networks are artificial intelligence architectures composed of multiple interconnected modules, allowing them to be more flexible and scalable compared to traditional neural networks. Each module can specialize in a specific task, facilitating learning and adaptation to different types of data and problems. This modularity allows networks to be organized hierarchically, where each module can learn particular features of the data and then combine their results to achieve a more robust solution. Additionally, the modular structure facilitates the reuse of components, which can reduce the time and resources needed to develop new applications. In the context of Deep Learning, these networks can integrate different types of neural networks, such as convolutional neural networks (CNNs), which are particularly effective in processing various types of data, including images and text. The ability of Modular Neural Networks to adapt and scale makes them a powerful tool in the field of artificial intelligence, enabling the tackling of complex problems more efficiently and effectively.
