https://nixustechnologies.com/hyperparameters-in-machine-learning/

1. A model’s behavior is controlled via hyperparameters, which are fine-
tuners or settings. A parameter whose value is chosen in advance of the
machine learning process is referred to as a hyperparameter.
Hyperparameters regulate the algorithms’ topology and degree of
complexity. Therefore, prior to actually fitting ML models to a data set,
hyperparameters must be carefully chosen.

2. Hyperparameters generally have two categories based on the purpose for
which they are being used.
Hyperparameter tuning and hyperparameter optimization are terms used
to describe the process of choosing the optimum hyperparameters to
utilize. To optimize the model, optimization parameters are applied.

3. Between the algorithm’s input and output, a hidden layer is present in
neural networks. In this layer, the function gives the input weights and
sends them via an activation function as the algorithm’s output. In
general, the network’s inputs are transformed nonlinearly by the hidden
layers. The neural network’s hidden layers can vary based on how it
performs, and in the same way, the layers could also differ based on the
weights they are associated with.

4. An essential component of any modern machine learning pipeline is
hyperparameters. The values assigned to the model before training
it on any data are known as the model’s magic numbers-
“hyperparameters”. It must be implemented properly though in
order to get speed improvements.