Videos

The PyTorch Profiler analyzes deep learning models’ performance by collecting timing and resource usage stats, helping identify bottlenecks and optimize memory and execution. Stanford’s CS25 lecture series, “Transformers United V4,” covers state-of-the-art transformer research and applications. XTuner offers a flexible toolkit for fine-tuning large models, supporting various algorithms and high training throughput. Luminal optimizes deep learning performance with ahead-of-time compilation and efficient execution on CUDA/Metal APIs. DeepFaceLive allows real-time face swaps from video streams, with options to train custom models and animate static faces.