A machine learning system is fed with input data—this can be numerical, textual, visual, or audiovisual. The system usually has an output—this can be a floating-point number, for instance, the acceleration of a self-driving car, or can be an integer representing a category (also called a class), for example, a cat or tiger from image recognition.
The main task of machine learning is to explore and construct algorithms that can learn from historical data and make predictions on new input data. For a data-driven solution, we need to define (or have it defined to us by an algorithm) an evaluation function called loss or cost function, which measures how well the models are learning. In this setup, we create an ...
