On a rainy Friday morning, the gentle drizzle caressed the earth. My daughter and I stepped out of the house, ready to take the bus to work and school, fulfilling our previous agreement. Suddenly, a thought flashed through my mind: we left the house later than usual, would we catch a later bus? Would the bus be unbearably crowded? Should we drive instead? But upon second thought, I decided to stick to our plan of taking the bus, leading by example and conveying the idea of perseverance.

The purpose of machine learning is to find a function through data that can map inputs to outputs. For example, given an image, this function can map the input image to corresponding descriptive text.

How do we find this function? The process of machine learning first involves setting the form of the model and the parameters to be used. Generally, models mostly use neural networks, and the parameters within are referred to as weights. A large model may have many parameters, some reaching billions. To find good parameters is beyond human capability, so the task is left to machines to compute. This computing process is known as training the model.