A significant challenge posed by deep learning algorithms is the requirement for substantial amounts of data to train models effectively. These algorithms, which are based on neural networks, perform well when large datasets are available, but they can underperform with limited data. Additionally, deep learning systems can be complex and opaque, making it difficult to interpret decision-making processes. This lack of transparency raises ethical concerns in applications such as healthcare and finance. Furthermore, computational resource demands for training deep learning models can lead to high operational costs.