What is characteristic of deep learning that traditional machine learning does not have?

One characteristic of deep learning that traditional machine learning does not have is the ability to automatically learn hierarchical representations of data. Deep learning models, such as deep neural networks, are able to learn multiple levels of abstraction in data, allowing them to capture complex patterns and relationships that may be difficult for traditional machine learning algorithms to uncover. This hierarchical representation learning is one of the key factors that has contributed to the success of deep learning in a wide range of applications, such as image and speech recognition, natural language processing, and more.

Multiple layers of abstraction

Traditional machine learning algorithms typically have a single layer of abstraction, meaning that they can only learn a single level of features from the data. Deep learning algorithms, on the other hand, have multiple layers of abstraction, which allows them to learn more complex features from the data. This makes deep learning algorithms more powerful than traditional machine learning algorithms, and it allows them to solve a wider range of problems.