Question - How is overfitting avoided in neural networks?
Answer -
Overfitting is avoided in neural nets by making use of a regularization technique called ‘dropout.’
By making use of the concept of dropouts, random neurons are dropped when the neural network is being trained to use the model doesn’t overfit. If the dropout value is too low, it will have a minimal effect. If it is too high, the model will have difficulty in learning.