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 life, live 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 life tie-ins are useful prompts for further reading.
Sophia Rossi • Editor
Feb 21, 2026
I’m usually wary of hype, but 101 Generative AI Projects: Diffusion Models, Transformers, ChatGPT, and Other LLMs (Paperback) earns it. The machine learning chapters are concrete enough to test.
Ethan Brooks • Professor
Feb 24, 2026
I read one section during a coffee break and ended up rewriting my plan for the week. The LLMs part hit that hard.
Sophia Rossi • Editor
Feb 20, 2026
Not perfect, but very useful. The oliver angle kept it grounded in current problems.
Ethan Brooks • Professor
Feb 23, 2026
A friend asked what I learned and I could actually explain it—because the Diffusion models chapter is built for recall.
Sophia Rossi • Editor
Feb 23, 2026
Not perfect, but very useful. The live angle kept it grounded in current problems.
Leo Sato • Automation
Feb 25, 2026
From a structural standpoint, the text creates a coherent ladder: definitions → examples → constraints → application. That’s why the ChatGPT arguments land.
Lina Ahmed • Product Manager
Feb 26, 2026
I’m usually wary of hype, but 101 Generative AI Projects: Diffusion Models, Transformers, ChatGPT, and Other LLMs (Paperback) earns it. The Diffusion models chapters are concrete enough to test.
Leo Sato • Automation
Feb 24, 2026
The book rewards re-reading. On pass two, the Diffusion models connections become more explicit and surprisingly rigorous.
Theo Grant • Security
Feb 25, 2026
From a structural standpoint, the text creates a coherent ladder: definitions → examples → constraints → application. That’s why the Generative AI arguments land.
Iris Novak • Writer
Feb 19, 2026
What surprised me: the advice doesn’t collapse under real constraints. The LLMs sections feel field-tested.
Theo Grant • Security
Feb 22, 2026
From a structural standpoint, the text creates a coherent ladder: definitions → examples → constraints → application. That’s why the LLMs arguments land.
Iris Novak • Writer
Feb 20, 2026
What surprised me: the advice doesn’t collapse under real constraints. The deep learning sections feel field-tested.
Harper Quinn • Librarian
Feb 26, 2026
If you enjoyed Introduction to Quantum Computing and Algorithms, this one scratches a similar itch—especially around third and momentum.
Ethan Brooks • Professor
Feb 18, 2026
A friend asked what I learned and I could actually explain it—because the open-source models chapter is built for recall.
Sophia Rossi • Editor
Feb 21, 2026
What surprised me: the advice doesn’t collapse under real constraints. The Generative AI sections feel field-tested.
Leo Sato • Automation
Feb 23, 2026
The book rewards re-reading. On pass two, the Diffusion models connections become more explicit and surprisingly rigorous.
Samira Khan • Founder
Feb 26, 2026
I’m usually wary of hype, but 101 Generative AI Projects: Diffusion Models, Transformers, ChatGPT, and Other LLMs (Paperback) earns it. The text generation chapters are concrete enough to test.
Nia Walker • Teacher
Feb 24, 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 Diffusion models made me instantly calmer about getting started.
Lina Ahmed • Product Manager
Feb 21, 2026
I’m usually wary of hype, but 101 Generative AI Projects: Diffusion Models, Transformers, ChatGPT, and Other LLMs (Paperback) earns it. The transformers chapters are concrete enough to test.
Leo Sato • Automation
Feb 26, 2026
If you care about conceptual clarity and transfer, the poem tie-ins are useful prompts for further reading.
Harper Quinn • Librarian
Feb 27, 2026
I read one section during a coffee break and ended up rewriting my plan for the week. The Generative AI part hit that hard.
Nia Walker • Teacher
Feb 18, 2026
This is the rare book where I highlight a lot, but I also use the highlights. The Generative AI sections feel super practical.
Lina Ahmed • Product Manager
Feb 25, 2026
Not perfect, but very useful. The infinite angle kept it grounded in current problems.
Leo Sato • Automation
Feb 23, 2026
The book rewards re-reading. On pass two, the transformers connections become more explicit and surprisingly rigorous.
Sophia Rossi • Editor
Feb 18, 2026
I’m usually wary of hype, but 101 Generative AI Projects: Diffusion Models, Transformers, ChatGPT, and Other LLMs (Paperback) earns it. The open-source models chapters are concrete enough to test.
Leo Sato • Automation
Feb 20, 2026
If you care about conceptual clarity and transfer, the third tie-ins are useful prompts for further reading.
Ava Patel • Student
Feb 22, 2026
Fast to start. Clear chapters. Great on Diffusion models.
Ethan Brooks • Professor
Feb 24, 2026
I read one section during a coffee break and ended up rewriting my plan for the week. The deep learning part hit that hard.
Theo Grant • Security
Feb 22, 2026
The book rewards re-reading. On pass two, the text generation connections become more explicit and surprisingly rigorous.
Benito Silva • Analyst
Feb 24, 2026
The book rewards re-reading. On pass two, the machine learning connections become more explicit and surprisingly rigorous.
Ava Patel • Student
Feb 20, 2026
Practical, not preachy. Loved the AI projects examples.
Ethan Brooks • Professor
Feb 24, 2026
A friend asked what I learned and I could actually explain it—because the transformers chapter is built for recall.
Theo Grant • Security
Feb 18, 2026
From a structural standpoint, the text creates a coherent ladder: definitions → examples → constraints → application. That’s why the Generative AI arguments land.
Nia Walker • Teacher
Feb 25, 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 transformers made me instantly calmer about getting started.
Lina Ahmed • Product Manager
Feb 24, 2026
What surprised me: the advice doesn’t collapse under real constraints. The ChatGPT sections feel field-tested.
Benito Silva • Analyst
Feb 19, 2026
From a structural standpoint, the text creates a coherent ladder: definitions → examples → constraints → application. That’s why the AI projects arguments land.
Leo Sato • Automation
Feb 22, 2026
The book rewards re-reading. On pass two, the open-source models connections become more explicit and surprisingly rigorous.
Sophia Rossi • Editor
Feb 24, 2026
What surprised me: the advice doesn’t collapse under real constraints. The LLMs sections feel field-tested.
Noah Kim • Indie Dev
Feb 26, 2026
If you enjoyed API Economy, this one scratches a similar itch—especially around poem and momentum.
Omar Reyes • Data Engineer
Feb 18, 2026
I’ve already recommended it twice. The machine learning chapter alone is worth the price. (Side note: if you like Introduction to Quantum Computing and Algorithms, you’ll likely enjoy this too.)
Nia Walker • Teacher
Feb 22, 2026
This is the rare book where I highlight a lot, but I also use the highlights. The LLMs sections feel super practical.
Lina Ahmed • Product Manager
Feb 22, 2026
I’m usually wary of hype, but 101 Generative AI Projects: Diffusion Models, Transformers, ChatGPT, and Other LLMs (Paperback) earns it. The machine learning chapters are concrete enough to test.
Ava Patel • Student
Feb 19, 2026
A solid “read → apply today” book. Also: infinite vibes.
Benito Silva • Analyst
Feb 26, 2026
From a structural standpoint, the text creates a coherent ladder: definitions → examples → constraints → application. That’s why the deep learning arguments land.
Ava Patel • Student
Feb 19, 2026
A solid “read → apply today” book. Also: oliver vibes.
Ethan Brooks • Professor
Feb 23, 2026
A friend asked what I learned and I could actually explain it—because the text generation chapter is built for recall.
Theo Grant • Security
Feb 18, 2026
The book rewards re-reading. On pass two, the machine learning connections become more explicit and surprisingly rigorous.
Nia Walker • Teacher
Feb 25, 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 machine learning made me instantly calmer about getting started.
Harper Quinn • Librarian
Feb 18, 2026
If you enjoyed Contacts and Constraints (Paperback), this one scratches a similar itch—especially around third and momentum.
Leo Sato • Automation
Feb 21, 2026
The book rewards re-reading. On pass two, the machine learning connections become more explicit and surprisingly rigorous.
Samira Khan • Founder
Feb 23, 2026
What surprised me: the advice doesn’t collapse under real constraints. The AI projects sections feel field-tested.
Noah Kim • Indie Dev
Feb 18, 2026
If you enjoyed Contacts and Constraints (Paperback), this one scratches a similar itch—especially around poem and momentum.
Omar Reyes • Data Engineer
Feb 21, 2026
I’ve already recommended it twice. The Diffusion models chapter alone is worth the price.
Maya Chen • UX Researcher
Feb 20, 2026
Not perfect, but very useful. The infinite angle kept it grounded in current problems.
Leo Sato • Automation
Feb 24, 2026
The book rewards re-reading. On pass two, the Diffusion models connections become more explicit and surprisingly rigorous.
Zoe Martin • Designer
Feb 24, 2026
What surprised me: the advice doesn’t collapse under real constraints. The AI projects sections feel field-tested.
Theo Grant • Security
Feb 26, 2026
If you care about conceptual clarity and transfer, the life tie-ins are useful prompts for further reading.
Maya Chen • UX Researcher
Feb 22, 2026
I’m usually wary of hype, but 101 Generative AI Projects: Diffusion Models, Transformers, ChatGPT, and Other LLMs (Paperback) earns it. The Diffusion models chapters are concrete enough to test.
Leo Sato • Automation
Feb 26, 2026
If you care about conceptual clarity and transfer, the life tie-ins are useful prompts for further reading.
Samira Khan • Founder
Feb 25, 2026
Not perfect, but very useful. The oliver angle kept it grounded in current problems.
Harper Quinn • Librarian
Feb 27, 2026
If you enjoyed Contacts and Constraints (Paperback), this one scratches a similar itch—especially around life and momentum.
Nia Walker • Teacher
Feb 27, 2026
It pairs nicely with what’s trending around live—you finish a chapter and think: “okay, I can do something with this.”
Ava Patel • Student
Feb 24, 2026
Practical, not preachy. Loved the ChatGPT examples.
Samira Khan • Founder
Feb 21, 2026
Not perfect, but very useful. The infinite angle kept it grounded in current problems.
Harper Quinn • Librarian
Feb 21, 2026
I read one section during a coffee break and ended up rewriting my plan for the week. The ChatGPT part hit that hard.
Nia Walker • Teacher
Feb 22, 2026
It pairs nicely with what’s trending around oliver—you finish a chapter and think: “okay, I can do something with this.”
Harper Quinn • Librarian
Feb 22, 2026
If you enjoyed API Economy, this one scratches a similar itch—especially around third and momentum.
Zoe Martin • Designer
Feb 20, 2026
I’m usually wary of hype, but 101 Generative AI Projects: Diffusion Models, Transformers, ChatGPT, and Other LLMs (Paperback) earns it. The Diffusion models chapters are concrete enough to test.
Harper Quinn • Librarian
Feb 18, 2026
If you enjoyed Introduction to Quantum Computing and Algorithms, this one scratches a similar itch—especially around life and momentum.
Leo Sato • Automation
Feb 22, 2026
From a structural standpoint, the text creates a coherent ladder: definitions → examples → constraints → application. That’s why the AI projects arguments land.
Zoe Martin • Designer
Feb 20, 2026
What surprised me: the advice doesn’t collapse under real constraints. The ChatGPT sections feel field-tested.
Theo Grant • Security
Feb 22, 2026
From a structural standpoint, the text creates a coherent ladder: definitions → examples → constraints → application. That’s why the ChatGPT arguments land.
Maya Chen • UX Researcher
Feb 20, 2026
What surprised me: the advice doesn’t collapse under real constraints. The AI projects sections feel field-tested.
Leo Sato • Automation
Feb 25, 2026
If you care about conceptual clarity and transfer, the life tie-ins are useful prompts for further reading.
Zoe Martin • Designer
Feb 25, 2026
I’m usually wary of hype, but 101 Generative AI Projects: Diffusion Models, Transformers, ChatGPT, and Other LLMs (Paperback) earns it. The machine learning chapters are concrete enough to test.
Harper Quinn • Librarian
Feb 21, 2026
A friend asked what I learned and I could actually explain it—because the machine learning chapter is built for recall.
Ethan Brooks • Professor
Feb 18, 2026
A friend asked what I learned and I could actually explain it—because the open-source models chapter is built for recall.
Zoe Martin • Designer
Feb 19, 2026
I’m usually wary of hype, but 101 Generative AI Projects: Diffusion Models, Transformers, ChatGPT, and Other LLMs (Paperback) earns it. The open-source models chapters are concrete enough to test.
Theo Grant • Security
Feb 19, 2026
If you care about conceptual clarity and transfer, the third tie-ins are useful prompts for further reading.
Maya Chen • UX Researcher
Feb 25, 2026
Not perfect, but very useful. The infinite angle kept it grounded in current problems.
Leo Sato • Automation
Feb 28, 2026
From a structural standpoint, the text creates a coherent ladder: definitions → examples → constraints → application. That’s why the deep learning arguments land.
Samira Khan • Founder
Feb 19, 2026
I’m usually wary of hype, but 101 Generative AI Projects: Diffusion Models, Transformers, ChatGPT, and Other LLMs (Paperback) earns it. The transformers chapters are concrete enough to test.
Lina Ahmed • Product Manager
Feb 21, 2026
I’m usually wary of hype, but 101 Generative AI Projects: Diffusion Models, Transformers, ChatGPT, and Other LLMs (Paperback) earns it. The transformers chapters are concrete enough to test.
Noah Kim • Indie Dev
Feb 28, 2026
I read one section during a coffee break and ended up rewriting my plan for the week. The AI projects part hit that hard.
Ava Patel • Student
Feb 27, 2026
Fast to start. Clear chapters. Great on transformers.
Ethan Brooks • Professor
Feb 26, 2026
I read one section during a coffee break and ended up rewriting my plan for the week. The AI projects part hit that hard.
Lina Ahmed • Product Manager
Feb 26, 2026
I’m usually wary of hype, but 101 Generative AI Projects: Diffusion Models, Transformers, ChatGPT, and Other LLMs (Paperback) earns it. The Diffusion models chapters are concrete enough to test.
Theo Grant • Security
Feb 21, 2026
If you care about conceptual clarity and transfer, the life tie-ins are useful prompts for further reading.
Maya Chen • UX Researcher
Feb 21, 2026
What surprised me: the advice doesn’t collapse under real constraints. The deep learning sections feel field-tested.
Iris Novak • Writer
Feb 22, 2026
Not perfect, but very useful. The infinite angle kept it grounded in current problems.
Omar Reyes • Data Engineer
Feb 21, 2026
Okay, wow. This is one of those books that makes you want to do things. The AI projects framing is chef’s kiss.
Maya Chen • UX Researcher
Feb 26, 2026
I’m usually wary of hype, but 101 Generative AI Projects: Diffusion Models, Transformers, ChatGPT, and Other LLMs (Paperback) earns it. The text generation chapters are concrete enough to test.
Leo Sato • Automation
Feb 26, 2026
If you care about conceptual clarity and transfer, the third tie-ins are useful prompts for further reading.
Samira Khan • Founder
Feb 24, 2026
What surprised me: the advice doesn’t collapse under real constraints. The Generative AI sections feel field-tested. (Side note: if you like Introduction to Quantum Computing and Algorithms, you’ll likely enjoy this too.)
Harper Quinn • Librarian
Feb 18, 2026
A friend asked what I learned and I could actually explain it—because the transformers chapter is built for recall.
Noah Kim • Indie Dev
Feb 20, 2026
If you enjoyed Contacts and Constraints (Paperback), this one scratches a similar itch—especially around poem and momentum.
Iris Novak • Writer
Feb 18, 2026
I’m usually wary of hype, but 101 Generative AI Projects: Diffusion Models, Transformers, ChatGPT, and Other LLMs (Paperback) earns it. The text generation chapters are concrete enough to test.
Benito Silva • Analyst
Feb 24, 2026
The book rewards re-reading. On pass two, the open-source models connections become more explicit and surprisingly rigorous.
Lina Ahmed • Product Manager
Feb 19, 2026
Not perfect, but very useful. The oliver angle kept it grounded in current problems.
Noah Kim • Indie Dev
Feb 19, 2026
I read one section during a coffee break and ended up rewriting my plan for the week. The ChatGPT part hit that hard.
Nia Walker • Teacher
Feb 22, 2026
This is the rare book where I highlight a lot, but I also use the highlights. The AI projects sections feel super practical.
Lina Ahmed • Product Manager
Feb 21, 2026
I’m usually wary of hype, but 101 Generative AI Projects: Diffusion Models, Transformers, ChatGPT, and Other LLMs (Paperback) earns it. The Diffusion models chapters are concrete enough to test.
Ava Patel • Student
Feb 28, 2026
Fast to start. Clear chapters. Great on text generation. (Side note: if you like Introduction to Quantum Computing and Algorithms, you’ll likely enjoy this too.)
Zoe Martin • Designer
Feb 19, 2026
I’m usually wary of hype, but 101 Generative AI Projects: Diffusion Models, Transformers, ChatGPT, and Other LLMs (Paperback) earns it. The Diffusion models chapters are concrete enough to test.
Sophia Rossi • Editor
Feb 19, 2026
Not perfect, but very useful. The live angle kept it grounded in current problems.
Maya Chen • UX Researcher
Feb 25, 2026
I’m usually wary of hype, but 101 Generative AI Projects: Diffusion Models, Transformers, ChatGPT, and Other LLMs (Paperback) earns it. The text generation chapters are concrete enough to test.
Ethan Brooks • Professor
Feb 24, 2026
A friend asked what I learned and I could actually explain it—because the machine learning chapter is built for recall.
Zoe Martin • Designer
Feb 25, 2026
What surprised me: the advice doesn’t collapse under real constraints. The deep learning sections feel field-tested.
Harper Quinn • Librarian
Feb 21, 2026
A friend asked what I learned and I could actually explain it—because the Diffusion models chapter is built for recall.
Noah Kim • Indie Dev
Feb 18, 2026
I read one section during a coffee break and ended up rewriting my plan for the week. The deep learning part hit that hard.
Iris Novak • Writer
Feb 26, 2026
Not perfect, but very useful. The oliver angle kept it grounded in current problems.
Benito Silva • Analyst
Feb 21, 2026
If you care about conceptual clarity and transfer, the third tie-ins are useful prompts for further reading.
Lina Ahmed • Product Manager
Feb 21, 2026
What surprised me: the advice doesn’t collapse under real constraints. The Generative AI sections feel field-tested.
Theo Grant • Security
Feb 19, 2026
The book rewards re-reading. On pass two, the text generation connections become more explicit and surprisingly rigorous.
Nia Walker • Teacher
Feb 22, 2026
It pairs nicely with what’s trending around infinite—you finish a chapter and think: “okay, I can do something with this.”
Harper Quinn • Librarian
Feb 25, 2026
I read one section during a coffee break and ended up rewriting my plan for the week. The deep learning part hit that hard.
Ava Patel • Student
Feb 24, 2026
Practical, not preachy. Loved the LLMs examples.
Zoe Martin • Designer
Feb 23, 2026
Not perfect, but very useful. The infinite angle kept it grounded in current problems.
Harper Quinn • Librarian
Feb 24, 2026
A friend asked what I learned and I could actually explain it—because the transformers chapter is built for recall.
Maya Chen • UX Researcher
Feb 21, 2026
I’m usually wary of hype, but 101 Generative AI Projects: Diffusion Models, Transformers, ChatGPT, and Other LLMs (Paperback) earns it. The transformers chapters are concrete enough to test.
Leo Sato • Automation
Feb 24, 2026
From a structural standpoint, the text creates a coherent ladder: definitions → examples → constraints → application. That’s why the Generative AI arguments land.
Samira Khan • Founder
Feb 25, 2026
What surprised me: the advice doesn’t collapse under real constraints. The ChatGPT sections feel field-tested.
Omar Reyes • Data Engineer
Feb 27, 2026
The life tie-ins made it feel like it was written for right now. Huge win. (Side note: if you like API Economy, you’ll likely enjoy this too.)
Iris Novak • Writer
Feb 20, 2026
What surprised me: the advice doesn’t collapse under real constraints. The deep learning sections feel field-tested.
Zoe Martin • Designer
Feb 22, 2026
Not perfect, but very useful. The oliver angle kept it grounded in current problems.
Sophia Rossi • Editor
Feb 26, 2026
What surprised me: the advice doesn’t collapse under real constraints. The ChatGPT sections feel field-tested.
Jules Nakamura • QA Lead
Feb 26, 2026
If you care about conceptual clarity and transfer, the third tie-ins are useful prompts for further reading.
Iris Novak • Writer
Feb 27, 2026
I’m usually wary of hype, but 101 Generative AI Projects: Diffusion Models, Transformers, ChatGPT, and Other LLMs (Paperback) earns it. The Diffusion models chapters are concrete enough to test.
Benito Silva • Analyst
Feb 23, 2026
If you care about conceptual clarity and transfer, the life tie-ins are useful prompts for further reading.
Sophia Rossi • Editor
Feb 21, 2026
What surprised me: the advice doesn’t collapse under real constraints. The Generative AI sections feel field-tested.
Noah Kim • Indie Dev
Feb 26, 2026
I read one section during a coffee break and ended up rewriting my plan for the week. The AI projects part hit that hard.
Nia Walker • Teacher
Feb 24, 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 transformers made me instantly calmer about getting started.
Samira Khan • Founder
Feb 20, 2026
Not perfect, but very useful. The infinite angle kept it grounded in current problems.
Harper Quinn • Librarian
Feb 18, 2026
I read one section during a coffee break and ended up rewriting my plan for the week. The ChatGPT part hit that hard.
Maya Chen • UX Researcher
Feb 26, 2026
What surprised me: the advice doesn’t collapse under real constraints. The AI projects sections feel field-tested.
Leo Sato • Automation
Feb 22, 2026
From a structural standpoint, the text creates a coherent ladder: definitions → examples → constraints → application. That’s why the LLMs arguments land.
Samira Khan • Founder
Feb 27, 2026
Not perfect, but very useful. The infinite angle kept it grounded in current problems.
Omar Reyes • Data Engineer
Feb 24, 2026
Okay, wow. This is one of those books that makes you want to do things. The ChatGPT framing is chef’s kiss.
Nia Walker • Teacher
Feb 21, 2026
It pairs nicely with what’s trending around oliver—you finish a chapter and think: “okay, I can do something with this.”
Benito Silva • Analyst
Feb 26, 2026
The book rewards re-reading. On pass two, the transformers connections become more explicit and surprisingly rigorous.
Lina Ahmed • Product Manager
Feb 19, 2026
What surprised me: the advice doesn’t collapse under real constraints. The ChatGPT sections feel field-tested.
Noah Kim • Indie Dev
Feb 26, 2026
I read one section during a coffee break and ended up rewriting my plan for the week. The deep learning part hit that hard.
Nia Walker • Teacher
Feb 21, 2026
This is the rare book where I highlight a lot, but I also use the highlights. The ChatGPT sections feel super practical.
Omar Reyes • Data Engineer
Feb 21, 2026
The third tie-ins made it feel like it was written for right now. Huge win. (Side note: if you like API Economy, you’ll likely enjoy this too.)
Nia Walker • Teacher
Feb 19, 2026
This is the rare book where I highlight a lot, but I also use the highlights. The deep learning sections feel super practical.
Sophia Rossi • Editor
Feb 26, 2026
Not perfect, but very useful. The oliver angle kept it grounded in current problems.
Noah Kim • Indie Dev
Feb 23, 2026
A friend asked what I learned and I could actually explain it—because the open-source models chapter is built for recall.
Nia Walker • Teacher
Feb 21, 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 machine learning made me instantly calmer about getting started.
Benito Silva • Analyst
Feb 26, 2026
The book rewards re-reading. On pass two, the transformers connections become more explicit and surprisingly rigorous.
Lina Ahmed • Product Manager
Feb 23, 2026
What surprised me: the advice doesn’t collapse under real constraints. The LLMs sections feel field-tested.
Theo Grant • Security
Feb 19, 2026
If you care about conceptual clarity and transfer, the poem tie-ins are useful prompts for further reading.
Jules Nakamura • QA Lead
Feb 24, 2026
If you care about conceptual clarity and transfer, the poem tie-ins are useful prompts for further reading.
Samira Khan • Founder
Feb 26, 2026
Not perfect, but very useful. The infinite angle kept it grounded in current problems.
Lina Ahmed • Product Manager
Feb 27, 2026
Not perfect, but very useful. The oliver angle kept it grounded in current problems.
Ava Patel • Student
Feb 22, 2026
Fast to start. Clear chapters. Great on machine learning.
Benito Silva • Analyst
Feb 19, 2026
The book rewards re-reading. On pass two, the text generation connections become more explicit and surprisingly rigorous.
Lina Ahmed • Product Manager
Feb 19, 2026
What surprised me: the advice doesn’t collapse under real constraints. The AI projects sections feel field-tested.
Theo Grant • Security
Feb 19, 2026
The book rewards re-reading. On pass two, the machine learning connections become more explicit and surprisingly rigorous.
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