Version: 1.2
The term “Artificial Intelligence” is everywhere — but it’s often misunderstood. Despite the name, these systems don’t actually possess intelligence or understanding. What’s typically called “AI” today refers to models that generate outputs based on patterns in data. They don’t comprehend meaning, context, or truth; they operate by predicting what comes next in a sequence, based on what they've seen before.
This textbook is open to everyone — no background in machine learning is required. If you’re comfortable with basic Python and introductory calculus, you’ll be able to follow along and engage with every concept. Whether you're encountering AI for the first time or revisiting it with a more critical perspective, our goal is to make the inner workings of these systems clear, approachable, and honest. We focus on what these tools actually do — and just as importantly, what they can’t.
You’ll explore core models and techniques, from basic neural networks to complex architectures like transformers, paired with hands-on coding exercises. Along the way, we emphasize the limitations, risks, and philosophical questions that come with generative models. By the end, you’ll not only have the skills to build these systems — you’ll also have the language to talk about them honestly!
This series covers the following topics, and you are encouraged to read the modules in order to build a strong foundation in the basics of AI.
This educational series has been meticulously crafted to serve a diverse audience of learners, from those taking their very first steps into artificial intelligence to those with prior exposure seeking to deepen their understanding. The curriculum follows a carefully designed progression that builds foundational knowledge while gradually introducing more complex concepts. This textbook was created for, and was used in, Georgia Tech’s CEE 4803 - Art & Generative AI course.
For beginners, we've taken special care to explain concepts clearly with intuitive examples and visualizations that make abstract ideas concrete. Meanwhile, more experienced learners will find sufficient depth and advanced material to expand their knowledge boundaries. If you already possess familiarity with certain fundamental topics, you're encouraged to navigate directly to modules that challenge your current expertise level.
This series represents our commitment to making high-quality AI education accessible to everyone, regardless of background or prior technical experience. 'AI' as we know it today, exists as a marketing term - we aim to democratize access to AI knowledge, foster critical thinking about AI's capabilities and limitations, and empower a new generation of innovators to apply these tools ethically and creatively. The interdisciplinary approach integrates perspectives from computer science, mathematics, engineering, and cognitive science to provide a comprehensive understanding of how artificial intelligence systems work and evolve. We sincerely hope this learning journey proves valuable as you explore the fascinating world of artificial intelligence, whether your goals involve academic advancement, professional development, or personal enrichment! Your feedback is welcomed as we continuously strive to improve and expand these educational resources.
This textbook features libre code samples and documentation. We respect your freedom. To accommodate license compatibility concerns, the project is dual-licensed under the GPLv3 and the GFDL 1.3. Users are encouraged to honor the principles of free software by ensuring full compliance with both licenses when using, modifying, or sharing this material.
The documentation in this repository is licensed under the GNU Free Documentation License 1.3 (GFDL 1.3). This means you are free to copy, modify, and distribute this document under the terms of the GFDL 1.3, provided that you retain this notice and provide attribution.
The code provided in this repository is licensed under the GNU General Public License v3.0 (GPLv3). This means you are free to use, modify, and distribute the code, provided that any derivative works also comply with the GPLv3 terms.
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Whether through code, comments, ideas, or documentation - we're committed to making this textbook the best it can be.
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Note
We've got just the thing for you! Check out our Perfomances Page for a collection of videos showcasing the results of our work. From generative art to AI-driven projects, you'll find a variety of content that highlights the potential of AI in creative applications.
This textbook was made with ❤️ in Atlanta, Georgia 🇺🇸 - Go Jackets! 🐝