Koulutusalgoritmin

Koulutusalgoritmi, often translated as training algorithm, refers to the computational process used to adjust the parameters of a machine learning model. This adjustment is performed based on a dataset, with the goal of enabling the model to learn patterns and make accurate predictions or decisions. The core idea behind a training algorithm is to minimize an error or loss function. This function quantifies how poorly the model is performing on the training data.

Various training algorithms exist, each with its own strengths and applications. Common examples include gradient descent and its variants like stochastic gradient descent (SGD) and Adam. Gradient descent iteratively updates the model's parameters in the direction that most rapidly reduces the loss function. Other algorithms, like backpropagation, are specifically designed for training artificial neural networks. The choice of training algorithm can significantly impact the speed of convergence, the final performance of the model, and its ability to generalize to unseen data. The process requires careful selection of hyperparameters, which are settings that are not learned from the data but are set before training begins. These hyperparameters can include learning rate, batch size, and regularization strength.