deep-learning

I treat PyTorch tensors like the main vocabulary of deep learning work. Understanding device placement, shape semantics, and autograd is more important than memorizing model classes. Once that foundation is solid, debugging training loops gets much ea

Transfer learning is the right default when labeled data is limited and time matters. I usually freeze the backbone first, train the head, then selectively unfreeze deeper layers if the domain gap justifies it. This strategy converges faster and is mu