‘Deep Learning’ was initially coined for neural networks that grew in breadth and depth, requiring increased computation power rather than new forms of ‘learning’. Yet, it turned out that deep learning indeed benefits from new twists in the learning stage. Nowadays it stands for a vast variety of complex architectural Artificial Intelligence (AI) models, which range from traditional pure neural networks to architectures which include neural networks for general processing, but are combined with application specific extensions, such as Convolutional Neural Networks (CNN) and transformer based Large Language Models (LLM). This course explains the core concepts of neural networks and deep learning. It further highlights challenges in the deep learning process, and it explores why and how neural networks contribute to larger architectures such as CNN, Generative Adversarial Network (GAN), and LLM.

This course is ideal for all who have little time but are interested in and need a solid understanding of deep learning as a fundamental technology driving many old, modern, and future AI systems and architectures. It explains deep learning and engineering jargon as well as technical details in a manner also suitable for non-IT audience. This course aims to separate fact from fiction and misunderstandings around deep learning; it puts you in a better position when you deal with any deep learning project, be it on systems, risks, or compliance concerns. As they say: AI is here to stay, so build up critical work knowledge now, for a baseline that allows you to keep up with this fast-paced technology.

This course complements our What to look out for when procuring an AI Solution - Legal and Technology Essentials course.

Who should attend?

• Privacy / Software Engineers, Technical Staff, Developers, Data Analysts, Data Architects, and Project / Risk Managers
• Data Protection Officers (DPOs) and Compliance Professionals
• Executives, Managers, and Staff involved in the management, collection, use or other processing of personal data

Course Details

Course Details

• The artificial neuron​
  • Structure
  • Operation
• Basic architecture of neural networks
  • Layers
  • Connections
  • AI ‘model’​
• Data flow in neural networks
  • Forward Pass
  • Backward Pass
  • Error Back Propagation
  • Hyperparameters
• The role of Graphic Processing Units (GPUs) in AI
• Challenges in deep learning
• Deep learning tricks and optimisations
• Overview of application specific extensions (such as for CNN, LLM, and GAN)

Course Facilitator

Albert Pichlmaier  Senior Cybersecurity and Privacy Engineer Senior Learning Technology Designer, Drew Data Protection & Cybersecurity Academy