Resources for teaching machine learning and deep learning
W&B is a great tool for learning and collaboration. We offer free academic accounts, and we've collected some good resources for helping you and your students navigate complex machine learning projects.
Features
Academic Teams
Request an academic team to get started having your students submit results to a shared workspace. Grading projects is easy when you can see the real, tracked results and the code that students used to generate models.
Reports
Ask your students to submit reports so you can explore their results and compare new projects against previous baselines. Reports make it easy to describe intermediate results and show progress, and all the graphs are connected to real model results that you can reproduce. View an example report →
Competitions
Create a project in your academic team, and have your students compete to achieve the best accuracy on a shared task. Here's a screenshot of an example competition. Each row is a different experiment, and users are competing for the highest accuracy. View the project →
Resources
Example script repo
We've built up a set of working examples of deep learning projects in different frameworks.
Full Stack Deep Learning: Taught by our friend Josh Tobin, this awesome course will take you to the next level of expertise in building deep learning models
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Stanford CS230 Deep Learning: Lectures and slides are available online for this awesome Stanford course taught by Andrew Ng.