Wrap-up video

Introduction to Deep Learning with PyTorch

Jasmin Ludolf

Senior Data Science Content Developer, DataCamp

Summary

  • Chapter 1

    • Discovered deep learning
    • Created small neural networks
    • Discovered linear layers

  • Chapter 2

    • Used loss and activation functions
    • Calculated derivatives
    • Use backpropagation

  • Chapter 3

    • Trained a neural network
    • Played with learning rate and momentum
    • And learned about their impact

  • Chapter 4

    • Strategies to improve your model
    • Reduced overfitting
    • Evaluated model performance
Introduction to Deep Learning with PyTorch

Next steps

Introduction to Deep Learning with PyTorch

Good luck!

Introduction to Deep Learning with PyTorch

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