A collection of “easy wins” to make machine learning in research reproducible.
This book focuses on basics that work. Getting you 90% of the way to top-tier reproducibility.
Every scientific conference has seen a massive uptick in applications that use some type of machine learning. Whether it’s a linear regression using scikit-learn, a transformer from Hugging Face, or a custom convolutional neural network in Jax, the breadth of applications is as vast as the quality of contributions.Visit Site Fork on Github See also: Speaker Emcee See also: Fellow