Full Stack Deep Learning
Full Stack Deep Learning
Full Stack Deep Learning
Course Content
Setting up Machine Learning Projects
Overview
Lifecycle
Prioritizing
Archetypes
Metrics
Baselines
Infrastructure and Tooling
Data Management
Machine Learning Teams
Training and Debugging
Testing and Deployment
Research Areas
Labs
Where to go next
Guest Lectures
Xavier Amatriain (Curai)
Chip Huyen (Snorkel)
Lukas Biewald (Weights & Biases)
Jeremy Howard (Fast.ai)
Richard Socher (Salesforce)
Raquel Urtasun (Uber ATG)
Yangqing Jia (Alibaba)
Andrej Karpathy (Tesla)
Jai Ranganathan (KeepTruckin)
Franziska Bell (Toyota Research)
Corporate Training and Certification
Corporate Training
Certification
Setting up Machine Learning Projects
How To Set Your Machine Learning Projects Up For Success
As always, please
submit a pull request
if any information is out of date!
Slides
Videos
Overview
/course-content/setting-up-machine-learning-projects/overview
Lifecycle
/course-content/setting-up-machine-learning-projects/lifecycle
Prioritizing
/course-content/setting-up-machine-learning-projects/prioritizing
Archetypes
/course-content/setting-up-machine-learning-projects/archetypes
Metrics
/course-content/setting-up-machine-learning-projects/metrics
Baselines
/course-content/setting-up-machine-learning-projects/baselines
​
​
Previous
Full Stack Deep Learning
Next
Overview
Last updated
10 months ago
Edit on GitHub
Contents
Slides
Videos
