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Perform data science on `data` that remains in `someone else's` server
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## PySyft in 10 minutes
-📝 API Example Notebooks
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-- 00-load-data.ipynb
-- 01-submit-code.ipynb
-- 02-review-code-and-approve.ipynb
-- 03-data-scientist-download-result.ipynb
-- 04-pytorch-example.ipynb
-- 05-custom-policy.ipynb
-- 06-multiple-code-requests.ipynb
-- 07-datasite-register-control-flow.ipynb
-- 08-code-version.ipynb
-- 09-blob-storage.ipynb
-- 10-container-images.ipynb
-- 11-container-images-k8s.ipynb
-- 12-custom-api-endpoint.ipynb
+📝 API Example Notebooks
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+- 00-load-data.ipynb
+- 01-submit-code.ipynb
+- 02-review-code-and-approve.ipynb
+- 03-data-scientist-download-result.ipynb
+- 04-pytorch-example.ipynb
+- 05-custom-policy.ipynb
+- 06-multiple-code-requests.ipynb
+- 07-datasite-register-control-flow.ipynb
+- 08-code-version.ipynb
+- 09-blob-storage.ipynb
+- 10-container-images.ipynb
+- 11-container-images-k8s.ipynb
+- 12-custom-api-endpoint.ipynb
## Deploy Kubernetes Helm Chart
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# What is Syft?
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`Syft` is OpenMined's `open source` stack that provides `secure` and `private` Data Science in Python. Syft decouples `private data` from model training, using techniques like [Federated Learning](https://ai.googleblog.com/2017/04/federated-learning-collaborative.html), [Differential Privacy](https://en.wikipedia.org/wiki/Differential_privacy), and [Encrypted Computation](https://en.wikipedia.org/wiki/Homomorphic_encryption). This is done with a `numpy`-like interface and integration with `Deep Learning` frameworks, so that you as a `Data Scientist` can maintain your current workflow while using these new `privacy-enhancing techniques`.
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🎥 PETs: Remote Data Science Unleashed - R gov 2021
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