Brief Introduction
A data scientist can only use AI to solve problems if they have enough training data. Whether you're at a startup or an enterprise, the most important and valuable problems are problems about people. Solving these problems using AI means having access to a large amount of private and sensitive data. Want to predict cancer in medical scans? If you're using traditional Deep Learning tools, this means persuading someone to send you a copy of a sensitive dataset. In many cases, this is either a non-Course Summary
Learn how to build secure and private AI models using TensorFlow and PyTorch. This course covers topics such as differential privacy, federated learning, and secure multi-party computation.Key Learning Points
- Understand the importance of privacy and security in AI models
- Learn how to use tools such as TensorFlow and PyTorch to build secure and private models
- Explore topics such as differential privacy, federated learning, and secure multi-party computation
Related Topics for further study
Learning Outcomes
- Understand the importance of privacy and security in AI models
- Learn how to build secure and private AI models using TensorFlow and PyTorch
- Gain knowledge in advanced topics such as differential privacy, federated learning, and secure multi-party computation
Prerequisites or good to have knowledge before taking this course
- Basic knowledge of Python
- Familiarity with machine learning concepts
Course Difficulty Level
IntermediateCourse Format
- Online
- Self-paced
Similar Courses
- Deep Learning
- Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning
Related Education Paths
Notable People in This Field
- Yann LeCun
- Cynthia Dwork
Related Books
Description
Learn how to extend PyTorch with the tools necessary to train AI models that preserve user privacy.Requirements
- To get the most out of your experience in this course, we recommend the following: Beginner-level skills in Deep Learning or Machine Learning Beginner-level skills in at least one Deep Learning framework (such as PyTorch) Beginner-level skills in Python No background in cryptography or advanced mathematics is required. See the Technology Requirements for using Udacity.
Knowledge
- Instructor videosLearn by doing exercisesTaught by industry professionals
Outline
- lesson 1 Differential Privacy Learn the mathematical definition of privacy Train AI models in PyTorch to learn public information from within private datasets lesson 2 Federated Learning Train on data that is highly distributed across multiple organizations and data centers using PyTorch and PySyft Aggregate gradients using a "trusted aggregator" lesson 3 Encrypted Computation Do arithmetic on encrypted numbers Use cryptography to share ownership over a number using Secret Sharing Leverage Additive Secret Sharing for encrypted Federated Learning
Summary of User Reviews
Discover the world of Secure and Private AI with this course from Udacity. Students have praised its comprehensive coverage of AI security and privacy, with a strong emphasis on practical applications. Overall, the course has received high ratings from satisfied users.Key Aspect Users Liked About This Course
The course is comprehensive and emphasizes practical applications.Pros from User Reviews
- The course covers a wide range of topics related to AI security and privacy.
- The content is well-structured and easy to follow.
- The instructors are knowledgeable and provide helpful feedback.
- The hands-on projects are engaging and offer practical experience.
- The course is up-to-date with the latest industry trends and developments.
Cons from User Reviews
- The course may be challenging for beginners with little background in AI.
- Some students have reported technical difficulties with the course platform.
- The course may require a significant time commitment.
- The assessments and grading criteria could be more transparent.
- Some students have reported that the course could benefit from more detailed explanations of certain concepts.
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