Batch normalization and Dropout for Deep Learning, explained with Examples!
This is week 4 of Build your own Research Internship in AI!
In this session we inspect the impact of Batch Normalization and dropout when translating semantic segmentation from one data set to another.
Existing videos on the theory of batch norm: • Why Does Batch Norm Work? (C2W3L06)
Theory of dropout: • Understanding Dropout (C2W1L07)
Next, we analyze the impact of data augmentation parameters on the segmentation evaluation metrics to identify the best combination for the task at hand!
Next session will be semantic segmentation for color medical images.