Translation: Model training is a process of refinement and improvement achieved by adjusting the model's weights and parameters. Training data is fed into the model, and these parameters are continually optimised until prediction errors are reduced. A model is considered successful when it has learned to make predictions with a high degree of accuracy. Training data could include lots of different images of cats. This can include cats that are different colours, different shapes and sizes, and seen from different angles. After learning from this training data, the model is successful when it can recognise that a new, previously unseen image also shows a cat.