Generative AI Fundamentals

Overview

Dive into generative AI with this course, which explores its fundamental principles and relationship to prior artificial intelligence innovations. We will walk through popular generative models and how they work, how deep learning models are developed using tools like PyTorch and Hugging Face, and finally, how to customize pre-trained open-source models for a specific use case.

In the project, you will apply a cutting-edge technique called parameter-efficient fine-tuning (PEFT), which allows for the adaptation of massive foundation models with minimal usage of computational resources.

Syllabus

• Introduction to Generative AI Fundamentals
This lesson provides the foundational knowledge needed about generative AI: what it is, how it's applied, and explanations of some popular algorithms and architectures for text and image generation.
• Deep Learning Fundamentals
This lesson covers the essentials of deep learning for the generative AI practitioner. From perceptrons to transfer learning including an introduction to the PyTorch and Hugging Face Python libraries.
• Foundation Models
This lesson explores foundation models in AI, how they differ from traditional models, how you can apply them to various tasks and evaluate their performance, and the ethical implication of their use.
• Adapting Foundation Models
This lesson covers a range of techniques for adapting foundation models, including prompt tuning, in-context learning, full fine-tuning, and parameter-efficient fine-tuning (PEFT).
• Apply Lightweight Fine-Tuning to a Foundation Model
Load and customize a Hugging Face foundation model using parameter-efficient fine-tuning. This technique allows you to harness the power of a pre-trained model for your custom task.

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Taught by

Brian Cruz

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Subjects

Computer Science