This comprehensive PyTorch course is designed for students, AI engineers, and researchers who want to gain practical and advanced expertise in deep learning and machine learning using PyTorch.

Starting with the fundamentals, learners explore tensors, automatic differentiation, and building basic neural networks. The course gradually moves to advanced topics including convolutional and recurrent networks, structured linear algebra, and deploying models with TorchServe.

Participants will learn distributed training techniques using Distributed Data Parallel (DDP), PiPPy, FSDP, and TorchCompile optimizations to handle large-scale models efficiently. Special modules cover generative AI, large language model (LLM) fine-tuning, and multimodal AI applications. Edge deployment using PyTorch Mobile and ExecuTorch ensures learners can run models on Android, iOS, and NPUs.

The curriculum emphasizes responsible AI practices, including fairness, bias reduction, and explainable AI. Real-world case studies and hackathon examples provide insights into practical deployments in cloud, enterprise, and edge environments. By the end of the course, learners will confidently build, optimize, and deploy AI models across a wide variety of applications, making them proficient in the modern PyTorch ecosystem.