Hyperparameter Tuning and AutoML Mastery: 15 Expert Techniques

Deep learning is a family of machine learning methods that use multi-layer neural networks to learn complex patterns in data.

Loss functions measure the difference between a model's predictions and actual values, while gradient descent iteratively adjusts model parameters to minimize this error by following the negative gradient of the function.

Hyperparameter tuning involves systematically searching for the optimal values for hyperparameters that control the learning process of a model, such as learning rate, batch size, and number of layers, to improve its accuracy and efficiency.