Modeling #
Hyperparameters Tuning #
Neural architecture search (NAS) is a technique for automating the design of artificial neural networks.
Trainable paramters
- learned by the algorithm during taining
- e.g. weights of a neural network
Hyperparameters
- set before launching the training
- not updated by the training itself
- e.g. learning rate, number of units in a dense layer
Even in a small algorithm the number of tunable hyperparameters can be significant. So the automation is key.
Example framework for automated tuning is Keras Tuner.