Linear Algebra Tutorial by an AI PhDㅣ2-Hour Full Course

This linear algebra tutorial, taught by an AI PhD and totaling about two hours, is tailored specifically for AI learners. The course is grounded in the fact that AI algorithms rely heavily on core linear algebra concepts such as matrix operations and vector spaces, yet many beginners often get stuck because textbooks are too abstract. Drawing on his own research experience, the instructor connects mathematical theory with real AI application scenarios—such as neural networks and data dimensionality reduction—balancing conceptual clarity with intuitive demonstrations, so that formulas become not just symbols but understandable tools. The key highlights: the entire video features high-definition voice recording with clear pacing; it progresses from basic definitions to key topics such as eigenvalues and singular value decomposition, accompanied by visual graphics and code examples to help viewers build geometric intuition. It suits developers with no background who want to get started in AI, students preparing for graduate entrance exams or interviews, and practitioners who need a quick refresher on mathematical foundations. Viewing advice: you don’t have to finish it in one sitting—study it section by section, and after each segment, try a few exercises or write a few lines of NumPy code for better results. Overall, this is an efficient learning resource that balances rigor and practicality.