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?
- Star PySyft on GitHub! - https://github.com/OpenMined/PySyft
You Will Learn:
- Part 1: The Basic Tools of Private, Decentralized Data Science
- Part 2: Intro to Federated Learning
- Part 3: Advanced Remote Execution Tools
- Part 4: Federated Learning via Trusted Aggregator
- Part 5: Intro to Encrypted Programs
- Part 6: Welcome to the Sandbox
- Part 7: Federate Learning with Federated Dataset
- Part 8: Federated Learning on MNIST using a CNN