In traditional machine learning, algorithms are designed to learn from data by identifying patterns and relationships between input features and output labels. However, these algorithms are typically limited to shallow models, which can only capture simple relationships between features.
Deep learning, on the other hand, enables machines to learn from data by identifying complex patterns and relationships between features. This is achieved by using neural networks with multiple layers, each of which can learn to represent more abstract and complex features.