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. Course Content
  2. Training and Debugging

Start Simple

How to start simple with deep learning models?

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Last updated 4 years ago

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Summary

  • Choose simple architecture:

    • LeNet/ResNet for images.

    • LSTM for sequences.

    • Fully-connected network with one hidden layer for all other tasks.

  • Use sensible hyper-parameter defaults:

    • Adam optimizer with a “magic” learning rate value of 3e-4.

    • ReLU activation for fully-connected and convolutional models and TanH activation for LSTM models.

    • He initialization for ReLU and Glorot initialization for TanH.

    • No regularization and data normalization.

  • Normalize data inputs: subtracting the mean and dividing by the variance.

  • Simplify the problem:

    • Working with a small training set around 10,000 examples.

    • Using a fixed number of objects, classes, input size, etc.

    • Creating a simpler synthetic training set like in research labs.

Start Simple - Troubleshooting