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adventuroussrv translated notebooks part 8 and part 8 bis (bengali) (#3096)
* Add Bengali Translated Notebooks Part 8 bis

ranslated Notebook:

-  Part 08 bis - Introduction to Protocols.ipynb

* add notebook 9
Latest commit 5fd29e8 Feb 26, 2020

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advanced Refactored code of SplitNN model and added evaluation on datasets (#2983 Jan 31, 2020
grid spelling correction in grid tutorial Part 01 (#3106) Feb 26, 2020
material Update tutorial + add illustration Jul 31, 2019
translations translated notebooks part 8 and part 8 bis (bengali) (#3096) Feb 26, 2020
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Part 01 - The Basic Tools of Private Deep Learning.ipynb Update pytorch version to 1.3 in docs (#2857) Dec 27, 2019
Part 02 - Intro to Federated Learning.ipynb [MRG] Notebook test (#2755) Dec 7, 2019
Part 03 - Advanced Remote Execution Tools.ipynb Fix typo in tutorial 3 (#2851) Dec 27, 2019
Part 04 - Federated Learning via Trusted Aggregator.ipynb Fix typo in tutorial 4 (#2860) Dec 27, 2019
Part 05 - Welcome to the Sandbox.ipynb Moving PyGrid module to PySyft (#2760) Jan 15, 2020
Part 06 - Federated Learning on MNIST using a CNN.ipynb
Part 07 - Federated Learning with Federated Dataset.ipynb Moving PyGrid module to PySyft (#2760) Jan 15, 2020
Part 08 - Introduction to Plans.ipynb Update Tuto part 08 - Introduction to Plans (#2867) Dec 30, 2019
Part 08 bis - Introduction to Protocols.ipynb Typo (#2855) Dec 25, 2019
Part 09 - Intro to Encrypted Programs.ipynb Field Expansion Nov 26, 2019
Part 10 - Federated Learning with Secure Aggregation.ipynb Tutorial Notebook 10 note on numpy hook (#3022) Feb 18, 2020
Part 11 - Secure Deep Learning Classification.ipynb [MRG] Notebook test (#2755) Dec 7, 2019
Part 12 - Train an Encrypted Neural Network on Encrypted Data.ipynb [MRG] Notebook test (#2755) Dec 7, 2019
Part 12 bis - Encrypted Training on MNIST.ipynb Hindi Translation part 11,12 and 12-bis (#2902) Jan 17, 2020
Part 13a - Secure Classification with Syft Keras and TFE - Public Training.ipynb [MRG] Notebook test (#2755) Dec 7, 2019
Part 13b - Secure Classification with Syft Keras and TFE - Secure Model Serving.ipynb [MRG] Notebook test (#2755) Dec 7, 2019
Part 13c - Secure Classification with Syft Keras and TFE - Private Prediction Client.ipynb Merge branch 'dev' into tfe-cluster Jul 4, 2019
README.md

README.md

Privacy Preserving Deep Learning with PyTorch & PySyft

In this step-by-step tutorial series, you'll learn about all the ways PySyft can be used to bring Privacy and Decentralization to the Deep Learning ecosystem. This tutorial series is continually updated with new features as they are implemented, and is designed for complete beginners.

Why Take This Tutorial?

1) A Competitive Career Advantage - For the past 20 years, the digital revolution has made data more and more accessible in ever larger quantities as analog processes have become digitized. However, with new regulation such as GDPR, enterprises are under pressure to have less freedom with how they use - and more importantly how they analyze - personal information. Bottom Line: Data Scientists aren't going to have access to as much data with "old school" tools, but by learning the tools for privacy preserving Deep Learning, YOU can be ahead of this curve and have a competitive advantage in your career.

2) Entrepreneurial Opportunities - There are a whole host of problems in society that Deep Learning can solve, but many of the most important haven't been explored because it would require access to incredibly sensitive information about people (consider using Deep Learning to help people with mental or relationship issues!). Thus, learning Private Deep Learning unlocks a whole host of new startup opportunities for you which were not previously available to others without these toolsets.

3) Social Good - Deep Learning can be used to solve a wide variety of problems in the real world, but Deep Learning on personal information is Deep Learning about people, for people. Learning how to do Deep Learning on data you don't own represents more than a career or entrepreneurial opportunity, it is the opportunity to help solve some of the most personal and important problems in people's lives - and to do it at scale.

How do I get extra credit?

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