101 Generative AI Projects: Diffusion Models, Transformers, ChatGPT, and Other LLMs (Paperback)
If you want practical clarity, this is a strong pick: Generative AI, Diffusion models, ChatGPT, transformers presented in a way that turns into decisions, not just notes.
ISBN: 9798291798089 Published: July 10, 2025 Generative AI, Diffusion models, ChatGPT, transformers, LLMs, machine learning, deep learning, text generation, AI projects, open-source models
What you’ll learn
Build confidence with ChatGPT-level practice.
Spot patterns in Diffusion models faster.
Turn deep learning into repeatable habits.
Connect ideas to read, 2026 without the overwhelm.
Who it’s for
Students who need structure and memorable examples. Skimmers and deep divers both win—chapters work standalone.
How to use it
Skim the headings, then re-read only what sparks a decision. Bonus: end sessions mid-paragraph to make restarting easy.
If you care about conceptual clarity and transfer, the time tie-ins are useful prompts for further reading. (Side note: if you like Introduction to Computational Cancer Biology, you’ll likely enjoy this too.)
Theo Grant • Security
Feb 12, 2026
Fast to start. Clear chapters. Great on Generative AI.
Samira Khan • Founder
Feb 14, 2026
A friend asked what I learned and I could actually explain it—because the LLMs chapter is built for recall.
Theo Grant • Security
Feb 11, 2026
Fast to start. Clear chapters. Great on ChatGPT.
Iris Novak • Writer
Feb 12, 2026
From a structural standpoint, the text creates a coherent ladder: definitions → examples → constraints → application. That’s why the open-source models arguments land.
Sophia Rossi • Editor
Feb 7, 2026
The book rewards re-reading. On pass two, the AI projects connections become more explicit and surprisingly rigorous.
Ethan Brooks • Professor
Feb 10, 2026
I’m usually wary of hype, but 101 Generative AI Projects: Diffusion Models, Transformers, ChatGPT, and Other LLMs (Paperback) earns it. The deep learning chapters are concrete enough to test.
Ava Patel • Student
Feb 16, 2026
Okay, wow. This is one of those books that makes you want to do things. The open-source models framing is chef’s kiss.
Ethan Brooks • Professor
Feb 17, 2026
What surprised me: the advice doesn’t collapse under real constraints. The open-source models sections feel field-tested.
Sophia Rossi • Editor
Feb 16, 2026
If you care about conceptual clarity and transfer, the stephen tie-ins are useful prompts for further reading.
Samira Khan • Founder
Feb 13, 2026
A friend asked what I learned and I could actually explain it—because the Generative AI chapter is built for recall.
Noah Kim • Indie Dev
Feb 13, 2026
I didn’t expect 101 Generative AI Projects: Diffusion Models, Transformers, ChatGPT, and Other LLMs (Paperback) to be this approachable. The way it frames ChatGPT made me instantly calmer about getting started.
Zoe Martin • Designer
Feb 7, 2026
The book rewards re-reading. On pass two, the Generative AI connections become more explicit and surprisingly rigorous.
Noah Kim • Indie Dev
Feb 14, 2026
I didn’t expect 101 Generative AI Projects: Diffusion Models, Transformers, ChatGPT, and Other LLMs (Paperback) to be this approachable. The way it frames AI projects made me instantly calmer about getting started.
Samira Khan • Founder
Feb 9, 2026
A friend asked what I learned and I could actually explain it—because the AI projects chapter is built for recall.
Theo Grant • Security
Feb 13, 2026
A solid “read → apply today” book. Also: read vibes.
Samira Khan • Founder
Feb 17, 2026
If you enjoyed Generative Adversarial Networks (GANs) Explained, this one scratches a similar itch—especially around time and momentum.
Noah Kim • Indie Dev
Feb 13, 2026
This is the rare book where I highlight a lot, but I also use the highlights. The text generation sections feel super practical.
Sophia Rossi • Editor
Feb 14, 2026
If you care about conceptual clarity and transfer, the time tie-ins are useful prompts for further reading.
Noah Kim • Indie Dev
Feb 8, 2026
This is the rare book where I highlight a lot, but I also use the highlights. The open-source models sections feel super practical.
Samira Khan • Founder
Feb 12, 2026
I read one section during a coffee break and ended up rewriting my plan for the week. The text generation part hit that hard.
Maya Chen • UX Researcher
Feb 9, 2026
If you care about conceptual clarity and transfer, the 2026 tie-ins are useful prompts for further reading.
Omar Reyes • Data Engineer
Feb 17, 2026
It pairs nicely with what’s trending around excerpt—you finish a chapter and think: “okay, I can do something with this.”
Maya Chen • UX Researcher
Feb 15, 2026
From a structural standpoint, the text creates a coherent ladder: definitions → examples → constraints → application. That’s why the machine learning arguments land.
Omar Reyes • Data Engineer
Feb 16, 2026
This is the rare book where I highlight a lot, but I also use the highlights. The machine learning sections feel super practical.
Jules Nakamura • QA Lead
Feb 8, 2026
I’m usually wary of hype, but 101 Generative AI Projects: Diffusion Models, Transformers, ChatGPT, and Other LLMs (Paperback) earns it. The LLMs chapters are concrete enough to test.
Omar Reyes • Data Engineer
Feb 13, 2026
I didn’t expect 101 Generative AI Projects: Diffusion Models, Transformers, ChatGPT, and Other LLMs (Paperback) to be this approachable. The way it frames deep learning made me instantly calmer about getting started.
Jules Nakamura • QA Lead
Feb 11, 2026
What surprised me: the advice doesn’t collapse under real constraints. The transformers sections feel field-tested. (Side note: if you like Introduction to Computational Cancer Biology, you’ll likely enjoy this too.)
Harper Quinn • Librarian
Feb 14, 2026
It pairs nicely with what’s trending around read—you finish a chapter and think: “okay, I can do something with this.”
Iris Novak • Writer
Feb 14, 2026
From a structural standpoint, the text creates a coherent ladder: definitions → examples → constraints → application. That’s why the transformers arguments land.
Noah Kim • Indie Dev
Feb 9, 2026
This is the rare book where I highlight a lot, but I also use the highlights. The text generation sections feel super practical.
Nia Walker • Teacher
Feb 15, 2026
The book rewards re-reading. On pass two, the LLMs connections become more explicit and surprisingly rigorous.
Lina Ahmed • Product Manager
Feb 9, 2026
I read one section during a coffee break and ended up rewriting my plan for the week. The Diffusion models part hit that hard.
Nia Walker • Teacher
Feb 11, 2026
From a structural standpoint, the text creates a coherent ladder: definitions → examples → constraints → application. That’s why the Diffusion models arguments land.
Ava Patel • Student
Feb 11, 2026
I’ve already recommended it twice. The LLMs chapter alone is worth the price.
Ethan Brooks • Professor
Feb 14, 2026
What surprised me: the advice doesn’t collapse under real constraints. The machine learning sections feel field-tested.
Sophia Rossi • Editor
Feb 16, 2026
The book rewards re-reading. On pass two, the ChatGPT connections become more explicit and surprisingly rigorous.
Samira Khan • Founder
Feb 15, 2026
If you enjoyed Introduction to Computational Cancer Biology, this one scratches a similar itch—especially around time and momentum.
Theo Grant • Security
Feb 16, 2026
Practical, not preachy. Loved the Diffusion models examples.
Iris Novak • Writer
Feb 10, 2026
From a structural standpoint, the text creates a coherent ladder: definitions → examples → constraints → application. That’s why the transformers arguments land.
Benito Silva • Analyst
Feb 16, 2026
Practical, not preachy. Loved the machine learning examples. (Side note: if you like Generative Adversarial Networks (GANs) Explained, you’ll likely enjoy this too.)
Maya Chen • UX Researcher
Feb 10, 2026
The book rewards re-reading. On pass two, the deep learning connections become more explicit and surprisingly rigorous.
Omar Reyes • Data Engineer
Feb 13, 2026
I didn’t expect 101 Generative AI Projects: Diffusion Models, Transformers, ChatGPT, and Other LLMs (Paperback) to be this approachable. The way it frames Generative AI made me instantly calmer about getting started.
Maya Chen • UX Researcher
Feb 16, 2026
The book rewards re-reading. On pass two, the deep learning connections become more explicit and surprisingly rigorous.
Iris Novak • Writer
Feb 11, 2026
The book rewards re-reading. On pass two, the AI projects connections become more explicit and surprisingly rigorous.
Omar Reyes • Data Engineer
Feb 11, 2026
This is the rare book where I highlight a lot, but I also use the highlights. The Diffusion models sections feel super practical.
Leo Sato • Automation
Feb 12, 2026
This is the rare book where I highlight a lot, but I also use the highlights. The transformers sections feel super practical.
Harper Quinn • Librarian
Feb 16, 2026
I didn’t expect 101 Generative AI Projects: Diffusion Models, Transformers, ChatGPT, and Other LLMs (Paperback) to be this approachable. The way it frames AI projects made me instantly calmer about getting started.
Ava Patel • Student
Feb 15, 2026
Okay, wow. This is one of those books that makes you want to do things. The transformers framing is chef’s kiss.
Benito Silva • Analyst
Feb 8, 2026
Fast to start. Clear chapters. Great on LLMs.
Maya Chen • UX Researcher
Feb 15, 2026
From a structural standpoint, the text creates a coherent ladder: definitions → examples → constraints → application. That’s why the open-source models arguments land.
Leo Sato • Automation
Feb 9, 2026
I didn’t expect 101 Generative AI Projects: Diffusion Models, Transformers, ChatGPT, and Other LLMs (Paperback) to be this approachable. The way it frames deep learning made me instantly calmer about getting started.
Samira Khan • Founder
Feb 16, 2026
If you enjoyed Introduction to Computational Cancer Biology, this one scratches a similar itch—especially around 2026 and momentum.
Jules Nakamura • QA Lead
Feb 14, 2026
I’m usually wary of hype, but 101 Generative AI Projects: Diffusion Models, Transformers, ChatGPT, and Other LLMs (Paperback) earns it. The Generative AI chapters are concrete enough to test.
Zoe Martin • Designer
Feb 14, 2026
From a structural standpoint, the text creates a coherent ladder: definitions → examples → constraints → application. That’s why the text generation arguments land.
Noah Kim • Indie Dev
Feb 8, 2026
I didn’t expect 101 Generative AI Projects: Diffusion Models, Transformers, ChatGPT, and Other LLMs (Paperback) to be this approachable. The way it frames LLMs made me instantly calmer about getting started.
Noah Kim • Indie Dev
Feb 13, 2026
This is the rare book where I highlight a lot, but I also use the highlights. The open-source models sections feel super practical.
Iris Novak • Writer
Feb 8, 2026
The book rewards re-reading. On pass two, the AI projects connections become more explicit and surprisingly rigorous.
Benito Silva • Analyst
Feb 10, 2026
Practical, not preachy. Loved the open-source models examples.
Noah Kim • Indie Dev
Feb 12, 2026
This is the rare book where I highlight a lot, but I also use the highlights. The text generation sections feel super practical.
Iris Novak • Writer
Feb 12, 2026
The book rewards re-reading. On pass two, the LLMs connections become more explicit and surprisingly rigorous.
Benito Silva • Analyst
Feb 8, 2026
A solid “read → apply today” book. Also: romance vibes.
Jules Nakamura • QA Lead
Feb 11, 2026
Not perfect, but very useful. The excerpt angle kept it grounded in current problems.
Zoe Martin • Designer
Feb 16, 2026
If you care about conceptual clarity and transfer, the time tie-ins are useful prompts for further reading.
Harper Quinn • Librarian
Feb 17, 2026
It pairs nicely with what’s trending around excerpt—you finish a chapter and think: “okay, I can do something with this.”
Maya Chen • UX Researcher
Feb 14, 2026
If you care about conceptual clarity and transfer, the stephen tie-ins are useful prompts for further reading.
Ethan Brooks • Professor
Feb 16, 2026
Not perfect, but very useful. The read angle kept it grounded in current problems.
Ava Patel • Student
Feb 16, 2026
The stephen tie-ins made it feel like it was written for right now. Huge win.
Lina Ahmed • Product Manager
Feb 12, 2026
I read one section during a coffee break and ended up rewriting my plan for the week. The open-source models part hit that hard.
Ethan Brooks • Professor
Feb 15, 2026
I’m usually wary of hype, but 101 Generative AI Projects: Diffusion Models, Transformers, ChatGPT, and Other LLMs (Paperback) earns it. The Generative AI chapters are concrete enough to test.
Zoe Martin • Designer
Feb 16, 2026
The book rewards re-reading. On pass two, the LLMs connections become more explicit and surprisingly rigorous.
Sophia Rossi • Editor
Feb 9, 2026
From a structural standpoint, the text creates a coherent ladder: definitions → examples → constraints → application. That’s why the Diffusion models arguments land.
Maya Chen • UX Researcher
Feb 11, 2026
If you care about conceptual clarity and transfer, the time tie-ins are useful prompts for further reading.
Iris Novak • Writer
Feb 13, 2026
From a structural standpoint, the text creates a coherent ladder: definitions → examples → constraints → application. That’s why the machine learning arguments land. (Side note: if you like Foundations of Graphics & Compute - Volume 3: Computing (Hardback), you’ll likely enjoy this too.)
Omar Reyes • Data Engineer
Feb 13, 2026
This is the rare book where I highlight a lot, but I also use the highlights. The open-source models sections feel super practical.
Theo Grant • Security
Feb 8, 2026
A solid “read → apply today” book. Also: excerpt vibes.
Ethan Brooks • Professor
Feb 10, 2026
What surprised me: the advice doesn’t collapse under real constraints. The text generation sections feel field-tested. (Side note: if you like Introduction to Computational Cancer Biology, you’ll likely enjoy this too.)
Ava Patel • Student
Feb 12, 2026
The time tie-ins made it feel like it was written for right now. Huge win.
Samira Khan • Founder
Feb 8, 2026
A friend asked what I learned and I could actually explain it—because the ChatGPT chapter is built for recall.
Jules Nakamura • QA Lead
Feb 11, 2026
Not perfect, but very useful. The read angle kept it grounded in current problems.
Iris Novak • Writer
Feb 17, 2026
If you care about conceptual clarity and transfer, the time tie-ins are useful prompts for further reading.
Omar Reyes • Data Engineer
Feb 17, 2026
I didn’t expect 101 Generative AI Projects: Diffusion Models, Transformers, ChatGPT, and Other LLMs (Paperback) to be this approachable. The way it frames deep learning made me instantly calmer about getting started.
Sophia Rossi • Editor
Feb 10, 2026
If you care about conceptual clarity and transfer, the time tie-ins are useful prompts for further reading.
Jules Nakamura • QA Lead
Feb 13, 2026
I’m usually wary of hype, but 101 Generative AI Projects: Diffusion Models, Transformers, ChatGPT, and Other LLMs (Paperback) earns it. The AI projects chapters are concrete enough to test.
Sophia Rossi • Editor
Feb 12, 2026
If you care about conceptual clarity and transfer, the time tie-ins are useful prompts for further reading.
Noah Kim • Indie Dev
Feb 16, 2026
It pairs nicely with what’s trending around romance—you finish a chapter and think: “okay, I can do something with this.”
Samira Khan • Founder
Feb 16, 2026
A friend asked what I learned and I could actually explain it—because the AI projects chapter is built for recall.
Omar Reyes • Data Engineer
Feb 13, 2026
It pairs nicely with what’s trending around romance—you finish a chapter and think: “okay, I can do something with this.”
Theo Grant • Security
Feb 15, 2026
A solid “read → apply today” book. Also: excerpt vibes.
Nia Walker • Teacher
Feb 8, 2026
If you care about conceptual clarity and transfer, the time tie-ins are useful prompts for further reading.
Benito Silva • Analyst
Feb 11, 2026
Fast to start. Clear chapters. Great on deep learning.
Iris Novak • Writer
Feb 16, 2026
From a structural standpoint, the text creates a coherent ladder: definitions → examples → constraints → application. That’s why the machine learning arguments land.
Benito Silva • Analyst
Feb 8, 2026
Practical, not preachy. Loved the transformers examples.
Noah Kim • Indie Dev
Feb 8, 2026
It pairs nicely with what’s trending around excerpt—you finish a chapter and think: “okay, I can do something with this.”
Leo Sato • Automation
Feb 12, 2026
I didn’t expect 101 Generative AI Projects: Diffusion Models, Transformers, ChatGPT, and Other LLMs (Paperback) to be this approachable. The way it frames ChatGPT made me instantly calmer about getting started.
Zoe Martin • Designer
Feb 13, 2026
If you care about conceptual clarity and transfer, the stephen tie-ins are useful prompts for further reading.
Theo Grant • Security
Feb 13, 2026
Practical, not preachy. Loved the open-source models examples.
Maya Chen • UX Researcher
Feb 10, 2026
If you care about conceptual clarity and transfer, the stephen tie-ins are useful prompts for further reading.
Leo Sato • Automation
Feb 9, 2026
It pairs nicely with what’s trending around read—you finish a chapter and think: “okay, I can do something with this.”
Zoe Martin • Designer
Feb 17, 2026
If you care about conceptual clarity and transfer, the time tie-ins are useful prompts for further reading.
Harper Quinn • Librarian
Feb 15, 2026
This is the rare book where I highlight a lot, but I also use the highlights. The open-source models sections feel super practical.
Noah Kim • Indie Dev
Feb 12, 2026
It pairs nicely with what’s trending around read—you finish a chapter and think: “okay, I can do something with this.”
Iris Novak • Writer
Feb 8, 2026
If you care about conceptual clarity and transfer, the 2026 tie-ins are useful prompts for further reading.
Benito Silva • Analyst
Feb 9, 2026
Practical, not preachy. Loved the text generation examples.
Jules Nakamura • QA Lead
Feb 11, 2026
Not perfect, but very useful. The romance angle kept it grounded in current problems.
Theo Grant • Security
Feb 14, 2026
Fast to start. Clear chapters. Great on ChatGPT.
Maya Chen • UX Researcher
Feb 8, 2026
The book rewards re-reading. On pass two, the LLMs connections become more explicit and surprisingly rigorous.
Leo Sato • Automation
Feb 16, 2026
This is the rare book where I highlight a lot, but I also use the highlights. The text generation sections feel super practical.
Zoe Martin • Designer
Feb 13, 2026
If you care about conceptual clarity and transfer, the time tie-ins are useful prompts for further reading.
Sophia Rossi • Editor
Feb 13, 2026
From a structural standpoint, the text creates a coherent ladder: definitions → examples → constraints → application. That’s why the open-source models arguments land.
Jules Nakamura • QA Lead
Feb 15, 2026
Not perfect, but very useful. The excerpt angle kept it grounded in current problems.
Iris Novak • Writer
Feb 9, 2026
The book rewards re-reading. On pass two, the Generative AI connections become more explicit and surprisingly rigorous.
Benito Silva • Analyst
Feb 10, 2026
Fast to start. Clear chapters. Great on AI projects.
Maya Chen • UX Researcher
Feb 11, 2026
From a structural standpoint, the text creates a coherent ladder: definitions → examples → constraints → application. That’s why the transformers arguments land.
Leo Sato • Automation
Feb 14, 2026
This is the rare book where I highlight a lot, but I also use the highlights. The machine learning sections feel super practical.
Zoe Martin • Designer
Feb 15, 2026
The book rewards re-reading. On pass two, the AI projects connections become more explicit and surprisingly rigorous.
Harper Quinn • Librarian
Feb 16, 2026
This is the rare book where I highlight a lot, but I also use the highlights. The Diffusion models sections feel super practical.
Maya Chen • UX Researcher
Feb 13, 2026
If you care about conceptual clarity and transfer, the time tie-ins are useful prompts for further reading.
Leo Sato • Automation
Feb 9, 2026
This is the rare book where I highlight a lot, but I also use the highlights. The machine learning sections feel super practical.
Samira Khan • Founder
Feb 16, 2026
If you enjoyed Generative Adversarial Networks (GANs) Explained, this one scratches a similar itch—especially around stephen and momentum. (Side note: if you like Generative Adversarial Networks (GANs) Explained, you’ll likely enjoy this too.)
Noah Kim • Indie Dev
Feb 12, 2026
It pairs nicely with what’s trending around excerpt—you finish a chapter and think: “okay, I can do something with this.”
Iris Novak • Writer
Feb 10, 2026
If you care about conceptual clarity and transfer, the stephen tie-ins are useful prompts for further reading.
Benito Silva • Analyst
Feb 16, 2026
A solid “read → apply today” book. Also: excerpt vibes.
Sophia Rossi • Editor
Feb 13, 2026
The book rewards re-reading. On pass two, the ChatGPT connections become more explicit and surprisingly rigorous.
Noah Kim • Indie Dev
Feb 10, 2026
I didn’t expect 101 Generative AI Projects: Diffusion Models, Transformers, ChatGPT, and Other LLMs (Paperback) to be this approachable. The way it frames Generative AI made me instantly calmer about getting started.
Nia Walker • Teacher
Feb 13, 2026
From a structural standpoint, the text creates a coherent ladder: definitions → examples → constraints → application. That’s why the transformers arguments land.
Ethan Brooks • Professor
Feb 7, 2026
I’m usually wary of hype, but 101 Generative AI Projects: Diffusion Models, Transformers, ChatGPT, and Other LLMs (Paperback) earns it. The ChatGPT chapters are concrete enough to test.
Theo Grant • Security
Feb 16, 2026
A solid “read → apply today” book. Also: excerpt vibes.
Jules Nakamura • QA Lead
Feb 16, 2026
Not perfect, but very useful. The romance angle kept it grounded in current problems.
Samira Khan • Founder
Feb 10, 2026
A friend asked what I learned and I could actually explain it—because the deep learning chapter is built for recall.
Ava Patel • Student
Feb 10, 2026
Okay, wow. This is one of those books that makes you want to do things. The Diffusion models framing is chef’s kiss.
Omar Reyes • Data Engineer
Feb 9, 2026
It pairs nicely with what’s trending around read—you finish a chapter and think: “okay, I can do something with this.”
Sophia Rossi • Editor
Feb 14, 2026
The book rewards re-reading. On pass two, the AI projects connections become more explicit and surprisingly rigorous.
Noah Kim • Indie Dev
Feb 15, 2026
This is the rare book where I highlight a lot, but I also use the highlights. The transformers sections feel super practical.
Nia Walker • Teacher
Feb 11, 2026
The book rewards re-reading. On pass two, the Generative AI connections become more explicit and surprisingly rigorous.
Demo thread: varied voice, nested replies, topic-matching language. Replace with real community posts if you collect them.
faq
Quick answers
Themes include Generative AI, Diffusion models, ChatGPT, transformers, LLMs, plus context from read, 2026, excerpt, time.
Use the Buy/View link near the cover. We also link to Goodreads search and the original source page.
Yes—use the Key Takeaways first, then read chapters in the order your curiosity pulls you.
Try 12 minutes reading + 3 minutes notes. Apply one idea the same day to lock it in.
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