What are declarative languages in machine learning?

What are declarative languages in machine learning?

Declarative languages focus on what the outcome should be rather than how to achieve it. In machine learning, they allow users to specify the desired results without detailing the steps to get there.

Can you give me some examples of declarative languages used in ML?

Sure! Examples include SQL for data querying, Prolog for logic programming, and TensorFlow's high-level APIs that allow for model definition without low-level coding.

How do declarative languages compare to imperative languages in ML?

Declarative languages emphasize the 'what' while imperative languages focus on the 'how'. This can lead to more concise and readable code in declarative languages, making them easier to use for certain tasks.

What are the advantages of using declarative languages in ML?

Advantages include improved readability, easier debugging, and the ability to focus on high-level logic rather than implementation details.