Deep Learning CNN Convolutional Neural Networks with Python - Batch Normalization

The video tutorial discusses the concept of mini batches in gradient descent, highlighting the assumption that all mini batches have the same distribution. It introduces the issue of covariate shift, where this assumption is violated, and presents batch normalization as a solution. Batch normalization standardizes features to have a mean of zero and a standard deviation of one, which helps in resolving covariate shift and provides regularization benefits. The process is applied layer-wise in neural networks, improving convergence speed and addressing gradient problems. The video concludes with a mention of upcoming topics on learning rates.

What is the main assumption about mini-batches in gradient descent?

• They are processed sequentially.

• They are independent of each other.

• They are distributed identically.

• They have different distributions.

What is the primary goal of batch normalization?

• To increase the learning rate.

• To eliminate the need for mini-batches.

• To standardize features to a common scale.

• To reduce the number of layers in a network.

How does batch normalization affect the convergence of gradient descent?

• It makes convergence unpredictable.

• It has no effect on convergence.

• It slows down convergence.

• It speeds up convergence.

What additional parameters are introduced by batch normalization?

• Fixed parameters for each layer.

• Static parameters for the entire network.

• Learnable parameters for each neuron.

• Random parameters for each batch.

What problem does batch normalization help to mitigate besides covariate shift?

• Data redundancy.

• Vanishing and exploding gradients.

• Overfitting.

• Underfitting.

In which layers is batch normalization applied?

• Only the input layer.

• Only hidden layers.

• Only the output layer.

• Every layer in the network.

Why is batch normalization considered a default choice for large datasets?

• It eliminates the need for mini-batches.

• It addresses covariate shift issues.

• It prevents overfitting.

• It reduces the size of the dataset.