Full Stack Deep Learning
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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
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Prioritizing
How do you decide which machine learning projects to work on?
Prioritizing - ML Projects
  • The project should have high impact, where cheap prediction is valuable for the complex parts of your business process.
  • The project should have high feasibility, which is driven by the data availability, accuracy requirements, and problem difficulty.
  • Here are 3 types of hard machine learning problems:
    • (1) The output is complex.
    • (2) Reliability is required.
    • (3) Generalization is expected.
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Archetypes
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