Secure and Private AI

Aperçu

Discover how to navigate the critical intersection of AI and privacy with our enlightening course on Secure and Private AI. In an era where the analysis of sensitive data, from predicting cancer survival rates to evaluating educational success, demands not only innovative methodologies but also stringent privacy measures, understanding how to access and use personal data without breaching user confidentiality has never been more important.

This course offers a deep dive into the forefront of privacy-preserving AI technologies, including Federated Learning, Differential Privacy, and Encrypted Computation.

Offered for free, you'll gain practical experience in employing the latest tools for safeguarding data privacy, such as OpenMined's PySyft. This revolutionary tool enhances widely-used Deep Learning frameworks like PyTorch, equipping them with the cryptographic and distributed computing capabilities needed to securely train AI models on confidential data dispersed across various locations.

Furthermore, participants are invited to join the Secure and Private AI Scholarship Challenge by Facebook, offering not only comprehensive course engagement but also the opportunity to compete for a scholarship in advanced Nanodegree programs such as Deep Learning or Computer Vision.

This course is made available through Udacity and falls under multiple categories including Artificial Intelligence Courses, Deep Learning Courses, PyTorch Courses, Cryptography Courses, and Federated Learning Courses, making it a perfect fit for learners aiming to master the balance between AI innovation and data privacy.

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Enseigné par

Andrew Trask

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