Full Stack Deep Learning
  • Full Stack Deep Learning
  • Course Content
    • Setting up Machine Learning Projects
      • Overview
      • Lifecycle
      • Prioritizing
      • Archetypes
      • Metrics
      • Baselines
    • Infrastructure and Tooling
      • Overview
      • Software Engineering
      • Computing and GPUs
      • Resource Management
      • Frameworks and Distributed Training
      • Experiment Management
      • Hyperparameter Tuning
      • All-in-one Solutions
    • Data Management
      • Overview
      • Sources
      • Labeling
      • Storage
      • Versioning
      • Processing
    • Machine Learning Teams
      • Overview
      • Roles
      • Team Structure
      • Managing Projects
      • Hiring
    • Training and Debugging
      • Overview
      • Start Simple
      • Debug
      • Evaluate
      • Improve
      • Tune
      • Conclusion
    • Testing and Deployment
      • Project Structure
      • ML Test Score
      • CI / Testing
      • Docker
      • Web Deployment
      • Monitoring
      • Hardware/Mobile
    • 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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  1. Guest Lectures

Lukas Biewald (Weights & Biases)

Lukas is co-founder and CEO of Weights & Biases, an ML tooling company. He previously co-founded and led data labeling company Figure Eight (acquired by Appen).

PreviousChip Huyen (Snorkel)NextJeremy Howard (Fast.ai)

Last updated 4 years ago

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  • Machine Learning can be unpredictable and opaque.

  • Deep Learning can be vulnerable to hacking.

  • Machine Learning requires tons of clean training data.

  • Deep Learning and GPUs break a lot of assumptions.

  • Machine Learning can look at far more data than humans.

  • The combination of humans and computers is powerful.

  • What's coming?

    • Better tools and platforms.

    • More medical applications.

    • New solutions to training data.

  • How to (successfully) ship deep learning projects:

    • Pay a lot of attention to your training data.

    • Get something working end-to-end right away, then improve one thing at a time.

    • Look for graceful ways to handle the inevitable cases where the algorithm fails.

Mentioned Resources:

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Read Lukas's article: ""

(credit to )

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