Reinforcement Learning and Deep RL Python Theory and Projects - DNN Batch Normalization

The video tutorial discusses batch normalization in the context of mini-batch gradient descent. It highlights the issue of covariate shift, where the training and test set distributions differ significantly. Batch normalization, applied after every layer or selectively, can mitigate this problem and also provide regularization to prevent overfitting. The decision of when to apply batch normalization is a hyperparameter that requires tuning. The tutorial concludes with a mention of implementing batch normalization using the TORCH framework.

What problem does batch normalization help to address in mini-batch gradient descent?

• High computational cost

• Slow convergence of the model

• Covariate shift between training and test sets

• Overfitting due to large datasets

What is a key benefit of applying batch normalization during training?

• It reduces the size of the dataset

• It eliminates the need for a learning rate

• It automatically selects the best model architecture

• It helps in learning normalization parameters

How does batch normalization contribute to regularization?

• By preventing overfitting

• By increasing the learning rate

• By normalizing the input data

• By reducing the model complexity

What is a challenge associated with batch normalization?

• Selecting the best activation function

• Deciding the size of the mini-batch

• Choosing the correct layers for normalization

• Determining the optimal learning rate

In which framework is batch normalization implemented in the next video?

• Caffe

• TORCH

• Keras

• TensorFlow