Nienna AI

What makes the self-attention mechanism in transformers more effective at capturing long-range dependencies compared to RNNs and CNNs, and what are the challenges in scaling these models for improved computational efficiency and robustness?

How do activation functions like ReLU and its variants compare to sigmoid and tanh in terms of solving the vanishing gradient problem?

How do you implement FSDP in PyTorch?

How can you code a custom attention mechanism in PyTorch?