Description: Hyperparameters of optimization are parameters that govern the training process of machine learning models, especially in the context of neural networks. These parameters are crucial for the model’s performance, as they influence how weights are adjusted during the learning process. Among the most common hyperparameters are the learning rate, which determines the magnitude of adjustments made to the model’s weights at each iteration; momentum, which helps accelerate the convergence process by smoothing updates; and batch size, which defines how many samples are used to compute the gradient at each optimization step. Other hyperparameters include the number of epochs, which indicates how many times the entire dataset is passed through, and the model architecture, which encompasses the number of layers and neurons in each layer. Proper selection of these hyperparameters is essential, as incorrect tuning can lead to issues such as overfitting or underfitting, negatively affecting the model’s ability to generalize to new data. Therefore, hyperparameter optimization is an active area of research and practice in the field of machine learning, where techniques such as random search and Bayesian optimization are used to find effective combinations that improve model performance.
