👉 The Continuous Deep Learning (CDT) math is a framework that extends traditional deep learning by incorporating continuous-time dynamics, allowing models to operate in real-time and handle temporal data more effectively. It leverages concepts from dynamical systems, such as differential equations and continuous-time processes, to model the evolution of neural network states over time. This approach enables the creation of recurrent neural networks (RNNs) that can process sequences and time-series data more efficiently, capturing temporal dependencies and adapting to changes in the input over time. By using continuous-time representations, CDT aims to improve the scalability and performance of deep learning models in applications like robotics, control systems, and real-time decision-making.
