What is batch normalization? What is dropout?

Question Analysis

The question is asking you to explain two important techniques used in training neural networks: batch normalization and dropout. These concepts are essential for improving the performance and efficiency of deep learning models. Understanding these techniques and their purposes will help in discussing how they contribute to the stability and generalization of neural networks.

Answer

Batch Normalization:

• Definition: Batch normalization is a technique for improving the training of deep neural networks by normalizing the inputs of each layer.
• Purpose: It helps in reducing internal covariate shift, which refers to the changes in the distribution of network activations due to changes in network parameters during training.
• How it Works: During training, it normalizes the input to each layer to have a mean of zero and a variance of one. This is done for each mini-batch of data.
• Benefits:
  • Speeds up training.
  • Allows for higher learning rates.
  • Reduces dependence on initialization.
  • Acts as a regularizer, sometimes eliminating the need for dropout.

Dropout:

• Definition: Dropout is a regularization technique used to prevent overfitting in neural networks by randomly setting a fraction of input units to zero at each update during training.
• Purpose: It helps in making the model more robust by preventing the network from becoming too reliant on specific neurons and encourages the network to learn more distributed representations.
• How it Works: During each training update, dropout randomly selects neurons to be "dropped out" or ignored with a certain probability (usually around 0.5 for hidden layers). During testing, no units are dropped out, and the full network is used.
• Benefits:
  • Reduces overfitting.
  • Encourages the network to learn more robust features.
  • Can be applied to any type of neural network layer.

By understanding these two techniques, you can discuss how they help improve the training and generalization of neural networks, which is crucial in machine learning interviews.