Handbook

Deep learning (DL) has been fuelling Artificial Intelligence (AI) and the Fourth Industrial Revolution in recent years. The success of DL in many applications, including generative AI such as ChatGPT or DALL·E, has gained rocketed attention and becomes a highly demanded skill across industries and sectors. It is transforming innovations, powering new applications and impact our society in everyday activities. In this unit, the students will learn the foundations of deep learning theory within a broader context of machine learning. At the same time, they will gain hands-on practical skills on how to apply DL to real-world applications across a range of AI cognitive tasks in computer vision such as image and object recognition, in natural language processing such as text classification using deep neural embeddings. Learning activities will focus on understand the fundamental concepts in DL such as neural networks (NN), convolutional NN, backpropagation and optimisation for deep learning, adversarial robustness, attention mechanism, transformer, important concepts in deep generative AI (VAE, GAN), in combination with laboratory sessions to gain hands-on experiences.