Learning Objectives

Thanks to improvements in computing power and scientific theory, generative AI is more accessible than ever before. Generative AI plays a significant role across industries due to its numerous applications, such as creative content generation, data augmentation, simulation and planning, anomaly detection, drug discovery, personalized recommendations, and more. In this course, learners will take a deeper dive into denoising diffusion models, which are a popular choice for text-to-image pipelines.

By participating in this workshop, you’ll:

  • Build a U-Net to generate images from pure noise
  • Improve the quality of generated images with the denoising diffusion process
  • Control the image output with context embeddings
  • Generate images from English text prompts using the Contrastive Language—Image Pretraining (CLIP) neural network

Prerequisites & Technologies Used

  • A basic understanding of Deep Learning Concepts.
  • Familiarity with a Deep Learning framework such as TensorFlow, PyTorch, or Keras. This course uses PyTorch.
  • Tools, libraries, frameworks used: PyTorch, CLIP
  • Hardware Requirements: Desktop or laptop computer capable of running the latest version of Chrome or Firefox. Each participant will be provided with dedicated access to a fully configured, GPU-accelerated server in the cloud. 
  • Only current students, researchers and faculty with a valid email address from a college or university in Europe, Israel, Middle East or Africa are eligible to attend.
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