Swift for TensorFlow is a next generation platform for deep learning and differentiable programming.
In general Swift for TF tries to resolve the drawbacks of Python
- Performance
- Concurrency
- Deployment
- Custom Ops
Swift has many advantages
- First-class autodiff
- Next-generation APIs
- High-quality tooling
- Python Integrability
- Performance of graphs + flexibility of Eager execution
| Name | Description | Notebook |
|---|---|---|
| Introduction | A basic introduction to Swift and its basic syntax |
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| Visualization | How to use Matplotlib in Swift For TensorFlow. |
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| Augmentation | How to use do basic augmentations like flipping and cropping |
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| MNIST | Using Swift for TensorFlow for training MNIST. |
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| CIFAR10 | Using Swift for TensorFlow for training CIFAR10. |
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| Transfer Learning | A basic transfer learning tutorial using VGG |
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| Deep Dream | Implementation of deep dream using a VGG model. |
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