Deep learning is now the foundation of all of machine learning and AI, making it more important than ever for students to obtain mastery of the empirical phenomena and experimental skills involved in deep neural networks. How can students acquire the skills necessary to invent the next generation of architectures or develop new broad theories of how deep learning works? This course takes the stance that knowledge or math abilities alone are not enough, and there is no substitute for getting your hands dirty and running many, many experiments. To this end, we will work through computationally tractable domains and take students through the process of obtaining mastery in this domain through efficient experimentation and experiment outcome prediction.
3-5 units · Letter or Credit/No Credit
Deep learning is now the foundation of all of machine learning and AI, making it more important than ever for students to obtain mastery of the empirical phenomena and experimental skills involved in deep neural networks. How can students acquire the skills necessary to invent the next generation of architectures or develop new broad theories of how deep learning works? This course takes the stance that knowledge or math abilities alone are not enough, and there is no substitute for getting your hands dirty and running many, many experiments. To this end, we will work through computationally tractable domains and take students through the process of obtaining mastery in this domain through efficient experimentation and experiment outcome prediction.
Offered in Autumn 2026 at Stanford University.