This advanced applied PyTorch course focuses on building real-world AI solutions through hackathons, deployment workflows, and responsible machine learning practices. The course is inspired by PyTorch Summer Hackathon projects and showcases how developers transform ideas into production-ready AI applications.

You will explore practical use cases including deploying machine learning models with TorchServe, optimizing models through pruning, and building mobile AI applications for Android and iOS using PyTorch Mobile. The course also covers PyTorch ecosystem libraries such as TorchVision for computer vision, TorchText for natural language processing, TorchAudio for audio-based AI, and PySlowFast for deep learning with video data.

Modern and emerging topics are a key focus, including Responsible AI, fairness measurement in machine learning systems, and ethical considerations when deploying AI at scale. You will also dive into advanced areas such as 3D deep learning with PyTorch3D, showcasing how PyTorch supports cutting-edge research and innovation.

By the end of this course, you will understand how to design, build, and deploy impactful AI applications using PyTorch, preparing you for hackathons, innovation challenges, and real-world AI engineering roles.