What are some advanced techniques in PyTorch for deep learning?

What are some advanced techniques in PyTorch for deep learning?

Some advanced techniques in PyTorch include transfer learning, model pruning, and mixed precision training.

Can you explain transfer learning and how to implement it in PyTorch?

Transfer learning involves taking a pre-trained model and fine-tuning it on a new dataset. In PyTorch, you can use models from torchvision and modify the final layers.

What is model pruning and why is it important?

Model pruning reduces the size of a model by removing weights that contribute little to its performance, making it faster and more efficient.

How does mixed precision training improve performance?

Mixed precision training uses both 16-bit and 32-bit floating-point types, which can speed up training and reduce memory usage without sacrificing accuracy.