Applied Deep Learning
A new, project-driven course for practitioners familiar with the fundamentals of deep learning.
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Build deep learning systems for the real world.

In a few years, deep learning has gone from obscure academic idea to deployment in products from translation to robotics. The technology has the potential for even broader application in fields like cybersecurity, agriculture, autonomous vehicles, climate change, healthcare, and basic science. To recognize this impact, the world needs more people who know how to deploy deep neural networks to solve real problems.
This program trains you to build deep learning systems that work in the real world. There are other great resources for learning theory, algorithms, and code frameworks. We expose students to the skills and tricks practitioners use to bridge the gap between theory and practice.

Josh Tobin

Join head instructor Josh Tobin and weekly guest instructors in exploring advanced approaches to applied deep learning. From identifying and scoping a project, to building a clean dataset, to troubleshooting training, to ensuring reproducible results, by the end of the program students will have completed a hands-on end-to-end project, collaborated on their work with some of industry’s and academia’s top deep learning practitioners, and formed an evergreen community tackling compelling, real-world problems.

Learn from the best

Guest instructors will speak on industry-relevant topics applicable to each week's curriculum.

Lukas Biewald

Pieter Abbeel

Paroma Varma

Rosanne Liu

Sergey Karayev

Lee Redden

Stuart Bowers

Peter Welinder

Neal Khosla


  • Week 1 - April 24

    Neal Khosla, Totemic

    Project selection and setup

    Scoping a project
  • Week 2 - May 1

    Stuart Bowers, Tesla
    Paroma Varma, Stanford AI

    Dataset design and management

    Creating a strong baseline on your initial dataset
  • Week 3 - May 9

    Lee Redden, Blue River Technologies


    Starting small and iteratively improving your model
  • Week 4 - May 15

    Sergey Karayev, Turnitin
    Lukas Biewald, Weights & Biases

    Infrastructure and tooling

    Setting up for parallel experimentation
  • Week 5 - May 29

    Pieter Abbeel, UC Berkeley

    Research areas

    Working on projects
  • Week 6 - June 5

    Peter Welinder, Open AI
    Rosanne Liu,

    Continuous integration and testing

    Writing tests for your codebase
  • Week 7 - June 12

    Project Presentations

Event Details

Dates: 4/24, 5/1, 5/9, 5/15, 5/29, 6/5, 6/12
Time: 6 - 8:30pm
Location: SF Design District
Cost: $2400 for working professionals, $600 for current students

Submit your application

If pricing is a hardship, tell us why and we will take that into consideration when evaluating applications. Please reach out to Stephanie at with any questions. It’s an exciting time to be in machine learning, hope to see you in our program!