What is normalization in deep learning and why is it important?

What is normalization in deep learning and why is it important?

Normalization in deep learning refers to techniques that adjust the input data or the outputs of layers to improve training speed and stability. It's important because it helps in reducing the risk of overfitting and allows the model to converge faster.

Can you explain different types of normalization techniques?

Sure! Common normalization techniques include Min-Max Scaling, Z-score Normalization, and Batch Normalization. Each has its own use cases and benefits.

How does Batch Normalization work?

Batch Normalization normalizes the output of a previous activation layer by subtracting the batch mean and dividing by the batch standard deviation. This helps in stabilizing the learning process.

Are there any downsides to using normalization?

Yes, while normalization can improve training, it may also introduce additional computational overhead and can sometimes lead to issues if not applied correctly.