This advanced PyTorch course is designed for AI professionals, researchers, and engineers aiming to leverage PyTorch 2.x and Lightning 2.0 for state-of-the-art AI applications. The course covers the latest innovations in generative AI, large language models (LLMs), diffusion models, and multi-modal AI workflows.

Participants will learn how to efficiently train and fine-tune LLMs, orchestrate distributed training with DDP and FSDP, and optimize inference on CPUs, GPUs, and Edge devices. The course also explores PyTorch Edge, ExecuTorch for on-device AI, TorchRL for reinforcement learning, and tools for bias reduction in AI models.

Additionally, learners will gain hands-on knowledge of PyTorch optimization techniques, including TorchCompile, TorchInductor, CUDA Graphs, and profiling with TorchBench. Lightning talks and keynotes from PyTorch conferences demonstrate real-world applications from leading companies like Meta, Intel, IBM, Hugging Face, and Microsoft.

By the end of this course, learners will be able to implement cutting-edge AI models, deploy them efficiently across cloud and Edge platforms, optimize training and inference pipelines, and contribute to high-performance, production-ready AI systems.