Hear from the experts and overview of HCAIM – virtual or physical attendance (Dublin).

(Optional) Privacy-Preserving Machine Learning (PPML) Hackathon – physical attendance (Dublin or Utrecht or Naples or Budapest)

When considering a Human-Centred perspective on AI, data privacy is a central concern. As AI and machine learning models are being trained on ever larger and more diverse datasets, and are being deployed more widely across society, there is a need for those AI models to protect both individual privacy as well as commercially sensitive data. However, while being able to effectively preserve privacy we do not want to also limit the capabilities of AI systems and this trade-off has been driving a number of exciting innovations and approaches.

Privacy-Preserving Machine Learning (PPML) is an area of Machine Learning that includes privacy-preserving methods such as Differential Privacy, Secure Multiparty Computation and Homomorphic Encryption amongst others. In this hackathon, you will get the opportunity to learn about these cutting edge PPML methods as well as explore their hands-on application across a range of real-world use cases. 

(Optional) NVIDIA Instructor-led workshop: Fundamentals of Deep Learning – virtual or physical attendance (Dublin or Naples or Utrecht or Budapest)

In this workshop, you’ll learn how deep learning works through hands-on exercises in computer vision and natural language processing. You’ll train deep learning models from scratch, learning tools and tricks to achieve accurate results. You’ll also learn to leverage freely available, state-of-the-art pre-trained models to save time and get your deep learning application up and running quickly.

Learning Objectives:

• Learn the fundamental techniques and tools required to train a deep learning model.
• Gain experience with common deep learning data types and model architectures.
• Enhance datasets through data augmentation to improve model accuracy.
• Leverage transfer learning between models to achieve efficient results with fewer data and computation.
• Build confidence to take on your project with a modern deep learning framework.