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Generative AI Fundamentals

Course

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.

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.

Intermediate

4 weeks

Real-world Projects

Completion Certificate

Last Updated February 13, 2024

Skills you'll learn:
Generative AI Fluency • Image classification • Transfer learning • Training neural networks
Prerequisites:
Intermediate Python

Course Lessons

Lesson 1

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.

Lesson 2

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.

Lesson 3

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.

Lesson 4

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).

Lesson 5 • Project

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.

Taught By The Best

Photo of Brian Cruz

Brian Cruz

Head of Core AI

Brian Cruz is the Head of Core AI at Samba TV, where he leads the initiative to use AI to improve the TV viewing experience. He formerly worked at Salesforce as a Machine Learning Engineer, creating models for forecasting sales revenue as part of Einstein Guidance. He has a degree in Pure Mathematics from UC Berkeley.

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