Depth First Learning: Learning to Understand Machine Learning
Abstract
An experimental science is only as strong as the veracity of its experiments – when
experiments are not reproducible, researchers struggle to place their work on solid
footing and progress is hampered. Many fields have had reproducibility crises. Some have recently recognized this as a problem in machine learning as well.
One way to make research more reproducible is to make it easier to develop a
deep understanding of the fundamentals underlying machine learning research
papers. In this work, we present a new pedagogy, Depth First Learning, that
addresses the challenges in understanding these fundamentals. In Depth First
Learning, papers are studied down to their core conceptual dependencies, with
additional material such as exercises and reproductions in code. We have used this
approach in classes and codified our curricula as an open-source website available
at http://www.depthfirstlearning.com.
experiments are not reproducible, researchers struggle to place their work on solid
footing and progress is hampered. Many fields have had reproducibility crises. Some have recently recognized this as a problem in machine learning as well.
One way to make research more reproducible is to make it easier to develop a
deep understanding of the fundamentals underlying machine learning research
papers. In this work, we present a new pedagogy, Depth First Learning, that
addresses the challenges in understanding these fundamentals. In Depth First
Learning, papers are studied down to their core conceptual dependencies, with
additional material such as exercises and reproductions in code. We have used this
approach in classes and codified our curricula as an open-source website available
at http://www.depthfirstlearning.com.
Research Areas
