Question - How can hyperparameters be trained in neural networks?
Answer -
Hyperparameters can be trained using four components as shown below:
- Batch size: This is used to denote the size of the input chunk. Batch sizes can be varied and cut into sub-batches based on the requirement.
- Epochs: An epoch denotes the number of times the training data is visible to the neural network so that it can train. Since the process is iterative, the number of epochs will vary based on the data.
- Momentum: Momentum is used to understand the next consecutive steps that occur with the current data being executed at hand. It is used to avoid oscillations when training.
- Learning rate: Learning rate is used as a parameter to denote the time required for the network to update the parameters and learn.
Next up on this top Deep Learning interview questions and answers blog, let us take a look at the intermediate questions.