cv is a collection of comprehensive deep learning study notes organized as Jupyter notebooks.
The project compiles educational material covering foundational and advanced deep learning topics, including PyTorch fundamentals, hands-on deep learning implementations, neural network theory, computer vision, natural language processing, large language models, and agent-based systems. The notes are structured as executable notebooks that combine explanatory text with code examples, allowing learners to understand concepts and run implementations in a single environment.
This resource suits developers and students building foundational knowledge in deep learning across multiple domains. It works well for those learning PyTorch as a framework, studying classical deep learning theory, or exploring modern applications like large language models and AI agents. The breadth of coverage—spanning computer vision, NLP, and agent systems—makes it useful for developers seeking a unified reference that connects theory from multiple authoritative sources rather than consulting separate courses individually.
The project is marked as complete, indicating the maintainer has finished adding core material. Development activity shows consistent engagement with the repository through updates and refinements to the notebook content. The codebase remains actively maintained with attention to keeping examples functional and relevant to current deep learning practices.
