Description: The training procedure is a systematic set of steps carried out to develop a machine learning model using a training dataset. This process involves selecting an appropriate algorithm, preparing the data, configuring the model parameters, and evaluating its performance. In supervised learning, the model is trained with labeled data, where each input has a known output, allowing the model to learn to make predictions. Hyperparameter optimization is a crucial part of the procedure, as it involves adjusting the model’s parameters to improve its performance. AutoML, or automated machine learning, aims to simplify this process, allowing models to be trained and optimized with minimal human intervention. On the other hand, Generative Adversarial Networks (GANs) use a training approach where two neural networks compete against each other, resulting in the generation of new and realistic data. In summary, the training procedure is fundamental for developing effective machine learning models and adapts to various approaches and techniques within the field.
