Andrew Ng’s DeepLearning.AI has announced a free short course on Prompt Engineering for Vision Models, offered in collaboration with Comet ML. This course is intended to give a comprehensive introduction to the concept of prompt engineering, specifically tailored for vision models.
It is one one-hour course under instruction by Abby Morgan (ML Engineer), Jacques Verré (Head of Product) and Caleb Kaiser (ML Engineer). To get the most out of this course, it is recommended to have a Python experience.
To stay relevant on the bleeding edge, you will promote different vision models, such as Meta’s Segment Anything Model (SAM), a universal image segmentation model, OWL-ViT, a zero-shot object detection model, and Stable Diffusion 2.0.
Participants will gain knowledge of how they can generate images efficiently using hyperparameters like strength, guidance scale, and number of inference steps.
Furthermore, you can select specific parts of an image by providing coordinates or drawing a box around the area you want to isolate and by combining all of these, you will be able to replace objects within an image with generated content.
The best part is you can generate custom images based on pictures of people or places that you provide using a fine-tuning technique called DreamBooth.
This course will utilise Comet, a library to track experiments and optimise visual prompt engineering workflows.
Previously, Andrew Ng had partnered with Google Cloud for the LLMOps to equip learners with the practical skills and knowledge needed to work with LLMs and build LLMOps pipelines in real-world applications.
