Deep Learning for Generative AI

We will cover the basic concepts of prominent methods for generative deep learning before starting a deep dive on their application to text, audio and images. Since we will focus on applications and the usage of respective foundation models and toolkits, we strongly recommend you get familiar with deep learning ahead of this course. In detail, we will cover:

Theory: Prominent generative Deep Learning Methods
-Generative Pretrained Transformers (GPT), Fine-Tuning, RLHF, Instruction Learning, Zero-Shot Learning, In-Context Learning, Chain-of-Thought
-Generative Adversarial Networks (GAN)
-Variational Auto-Encoders (VAE)
-Diffusion
-Style transfer

Hands-On:
-Text: GPT Prompt Engineering
-Text to Speech, Voice Conversion
-Image generation and captioning


We will conclude the course with an outlook on risks, limitations, ethical and legal implications.