Hyperparameter optimisation is the process of finding the best settings for a machine learning model to improve its performance. These settings, called hyperparameters, are not learned from the data but chosen before training begins. By carefully selecting these values, the model can make more accurate predictions and avoid problems like overfitting or underfitting.
Category: Model Optimisation Techniques
Knowledge Distillation
Knowledge distillation is a machine learning technique where a large, complex model teaches a smaller, simpler model to perform the same task. The large model, called the teacher, passes its knowledge to the smaller student model by providing guidance during training. This helps the student model achieve nearly the same performance as the teacher but…