Learning the 'Why' in Deep Learning
https://claude.ai/share/82ba8ee9-f673-4f8a-a89a-9fd380bd0e53 We all know about DL architectures, but do we know how researchers arrived at this !! How to arrive at a deep learning architecture ? How to make connections between layers and be intuitionally correct ? The logic behind a deep learning architecture , how to make the gradients flow from different parameters space and still making sense , and the use of residual connections , layer normalisation .. etc ...