Data Science and Machine Learning (Theory and Projects) A to Z - Machine Learning Models and Optimization: Machine Learn

The video tutorial explains the concepts of parameters and hyperparameters in machine learning models. It discusses how parameters are values estimated during the training process, while hyperparameters are decisions made before training, such as selecting a model class. The tutorial covers different model classes, including linear models, polynomial models, and neural networks, and emphasizes the importance of choosing the right model class. It also highlights the complexity of hyperparameter selection in deep learning, describing it as an art due to the numerous decisions required.

What new insight or understanding did you gain from this video?

Evaluate responses using AI:

OFF