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Welcome and Course Map

Welcome!

Start here: What is Generative AI?

Generative models 101: the big picture

How Generative AI Learns Patterns to Create New Content

Under the hood: how these models work

Training process: from data to model

Common training techniques and model types

Creative Uses and Real-World Impact

Real-world applications you’ll actually recognize

Benefits and challenges in everyday use

Ethics check: using AI responsibly

Check Your Understanding: Quizzes

Quiz 1: Generative AI basics

Quiz 2: How models learn patterns

Quiz 3: Training process and techniques

Quiz 4: Real-world applications

Quiz 5: Benefits and challenges

Quiz 6: Ethics and responsibility

Generative AI: Patterns, Creativity, Bias, and Societal Impact is a hands-on, plain-language course designed to help students and creators understand how modern AI systems learn from data to produce text, images, audio, and code. You’ll get a clear big-picture view of model families like transformers, diffusion models, VAEs, and GANs, then zoom into how they actually work—tokens, pixels, features, training vs. inference, and how prompts shape outputs. Beyond mechanics, we’ll translate technical ideas such as loss functions, optimization, and alignment into approachable concepts you can use in everyday work. Whether you’re brainstorming essays, prototyping game assets, composing music, or building assistive tools, you’ll learn to pick the right models, set realistic expectations, and get better results. We’ll also examine the real-world impact of generative AI—both benefits and trade-offs—including bias, privacy, intellectual property, and responsible use. Short, focused lessons, practical examples, and quick quizzes help you check understanding as you go. By the end, you’ll be able to reason about how generative AI creates content, apply it creatively and ethically, and speak confidently about its opportunities and risks in school, community, and professional contexts.

Generative AI: Patterns + Creativity

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            <p>Generative AI is a kind of technology that learns patterns from huge amounts of data—like words, images, sounds, and code—and then uses those patterns to make brand-new content. In this course, you’ll see how it can write essays, generate pictures and music, help with studying, and even translate languages. We’ll keep explanations simple and relatable, so you can connect what you learn to your life as a student and creator. You’ll start with the basics: what generative AI is, why it matters today, and where you’ve probably seen it already.</p>

<p>From there, we’ll peek under the hood to understand how these models actually work. You’ll learn about tokens and pixels (what models “see”), how they learn by predicting what comes next or by removing noise, and how prompts and sampling turn patterns into outputs. We’ll walk through the training process in plain language—datasets, loss functions, and fine-tuning—plus common model types like transformers, diffusion models, VAEs, and GANs. Along the way, you’ll explore real-world uses you’ll recognize, the benefits (speed, ideas, accessibility) and challenges (accuracy, bias, safety, copyright), and how to use these tools responsibly with fairness, consent, and transparency. Short quizzes in each unit will help you check your understanding and build confidence step by step.</p>
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