101 Generative AI Projects: Diffusion Models, Transformers, ChatGPT, and Other LLMs (Paperback)
Think of it as a friendly deep-dive into Generative AI, Diffusion models, ChatGPT, transformers—with enough structure to skim and enough depth to grow into.
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 2026, read 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.
The book rewards re-reading. On pass two, the open-source models connections become more explicit and surprisingly rigorous. (Side note: if you like Data Mining and Machine Learning Essentials, you’ll likely enjoy this too.)
Iris Novak • Writer
Feb 6, 2026
A solid “read → apply today” book. Also: trailer vibes.
Sophia Rossi • Editor
Feb 7, 2026
Fast to start. Clear chapters. Great on Diffusion models.
Ethan Brooks • Professor
Feb 4, 2026
I’ve already recommended it twice. The text generation chapter alone is worth the price.
Sophia Rossi • Editor
Jan 31, 2026
Fast to start. Clear chapters. Great on open-source models.
Iris Novak • Writer
Feb 7, 2026
A solid “read → apply today” book. Also: read vibes.
Ava Patel • Student
Feb 6, 2026
Practical, not preachy. Loved the ChatGPT examples.
Ethan Brooks • Professor
Feb 4, 2026
Okay, wow. This is one of those books that makes you want to do things. The ChatGPT framing is chef’s kiss.
Sophia Rossi • Editor
Jan 31, 2026
Fast to start. Clear chapters. Great on machine learning.
Iris Novak • Writer
Feb 7, 2026
Fast to start. Clear chapters. Great on transformers.
Theo Grant • Security
Jan 31, 2026
A friend asked what I learned and I could actually explain it—because the text generation chapter is built for recall.
Samira Khan • Founder
Jan 31, 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 open-source models made me instantly calmer about getting started.
Theo Grant • Security
Feb 6, 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.
Benito Silva • Analyst
Feb 4, 2026
If you enjoyed Foundations of Graphics & Compute - Volume 3: Computing (Hardback), this one scratches a similar itch—especially around february and momentum.
Ava Patel • Student
Feb 2, 2026
Practical, not preachy. Loved the AI projects examples.
Ethan Brooks • Professor
Feb 5, 2026
Okay, wow. This is one of those books that makes you want to do things. The Generative AI framing is chef’s kiss. (Side note: if you like Foundations of Graphics & Compute - Volume 3: Computing (Hardback), you’ll likely enjoy this too.)
Ava Patel • Student
Feb 6, 2026
A solid “read → apply today” book. Also: making vibes.
Benito Silva • Analyst
Jan 28, 2026
A friend asked what I learned and I could actually explain it—because the open-source models chapter is built for recall.
Ava Patel • Student
Feb 3, 2026
Practical, not preachy. Loved the ChatGPT examples.
Jules Nakamura • QA Lead
Feb 6, 2026
If you care about conceptual clarity and transfer, the 2026 tie-ins are useful prompts for further reading.
Omar Reyes • Data Engineer
Feb 6, 2026
The book rewards re-reading. On pass two, the machine learning connections become more explicit and surprisingly rigorous.
Jules Nakamura • QA Lead
Feb 2, 2026
The book rewards re-reading. On pass two, the text generation connections become more explicit and surprisingly rigorous. (Side note: if you like Foundations of Graphics & Compute - Volume 3: Computing (Hardback), you’ll likely enjoy this too.)
Harper Quinn • Librarian
Feb 1, 2026
From a structural standpoint, the text creates a coherent ladder: definitions → examples → constraints → application. That’s why the AI projects arguments land.
Ethan Brooks • Professor
Feb 6, 2026
Okay, wow. This is one of those books that makes you want to do things. The LLMs framing is chef’s kiss.
Theo Grant • Security
Feb 7, 2026
I read one section during a coffee break and ended up rewriting my plan for the week. The ChatGPT part hit that hard.
Zoe Martin • Designer
Feb 3, 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 text generation made me instantly calmer about getting started.
Noah Kim • Indie Dev
Jan 30, 2026
If you enjoyed Data Mining and Machine Learning Essentials, this one scratches a similar itch—especially around 2026 and momentum.
Theo Grant • Security
Jan 29, 2026
I read one section during a coffee break and ended up rewriting my plan for the week. The LLMs part hit that hard.
Samira Khan • Founder
Feb 3, 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.
Theo Grant • Security
Feb 3, 2026
If you enjoyed Shaders Unchained: Writing Powerful Shaders for Every Platform, this one scratches a similar itch—especially around 2026 and momentum.
Samira Khan • Founder
Feb 3, 2026
It pairs nicely with what’s trending around trailer—you finish a chapter and think: “okay, I can do something with this.”
Theo Grant • Security
Feb 2, 2026
A friend asked what I learned and I could actually explain it—because the transformers chapter is built for recall.
Zoe Martin • Designer
Feb 2, 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.
Noah Kim • Indie Dev
Feb 5, 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. (Side note: if you like Foundations of Graphics & Compute - Volume 3: Computing (Hardback), you’ll likely enjoy this too.)
Theo Grant • Security
Feb 1, 2026
A friend asked what I learned and I could actually explain it—because the Diffusion models chapter is built for recall.
Samira Khan • Founder
Feb 7, 2026
It pairs nicely with what’s trending around read—you finish a chapter and think: “okay, I can do something with this.”
Ava Patel • Student
Jan 30, 2026
A solid “read → apply today” book. Also: making vibes.
Nia Walker • Teacher
Feb 1, 2026
A solid “read → apply today” book. Also: trailer vibes. (Side note: if you like Shaders Unchained: Writing Powerful Shaders for Every Platform, you’ll likely enjoy this too.)
Benito Silva • Analyst
Feb 6, 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.
Maya Chen • UX Researcher
Feb 6, 2026
This is the rare book where I highlight a lot, but I also use the highlights. The LLMs sections feel super practical.
Benito Silva • Analyst
Jan 30, 2026
If you enjoyed Shaders Unchained: Writing Powerful Shaders for Every Platform, this one scratches a similar itch—especially around february and momentum.
Ava Patel • Student
Jan 30, 2026
A solid “read → apply today” book. Also: read vibes.
Jules Nakamura • QA Lead
Feb 2, 2026
From a structural standpoint, the text creates a coherent ladder: definitions → examples → constraints → application. That’s why the deep learning arguments land.
Lina Ahmed • Product Manager
Feb 5, 2026
What surprised me: the advice doesn’t collapse under real constraints. The LLMs sections feel field-tested.
Jules Nakamura • QA Lead
Feb 7, 2026
If you care about conceptual clarity and transfer, the 2026 tie-ins are useful prompts for further reading.
Iris Novak • Writer
Jan 30, 2026
Practical, not preachy. Loved the LLMs examples.
Harper Quinn • Librarian
Feb 4, 2026
The book rewards re-reading. On pass two, the Diffusion models connections become more explicit and surprisingly rigorous.
Iris Novak • Writer
Feb 3, 2026
Practical, not preachy. Loved the deep learning examples.
Harper Quinn • Librarian
Jan 29, 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 2, 2026
A solid “read → apply today” book. Also: trailer vibes.
Benito Silva • Analyst
Feb 1, 2026
A friend asked what I learned and I could actually explain it—because the open-source models chapter is built for recall.
Lina Ahmed • Product Manager
Jan 31, 2026
What surprised me: the advice doesn’t collapse under real constraints. The ChatGPT sections feel field-tested.
Leo Sato • Automation
Feb 4, 2026
If you enjoyed Data Mining and Machine Learning Essentials, this one scratches a similar itch—especially around week and momentum.
Sophia Rossi • Editor
Feb 1, 2026
A solid “read → apply today” book. Also: read vibes.
Noah Kim • Indie Dev
Feb 4, 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 7, 2026
This is the rare book where I highlight a lot, but I also use the highlights. The Generative AI sections feel super practical.
Leo Sato • Automation
Feb 6, 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.
Samira Khan • Founder
Feb 1, 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.
Omar Reyes • Data Engineer
Feb 3, 2026
The book rewards re-reading. On pass two, the transformers connections become more explicit and surprisingly rigorous.
Nia Walker • Teacher
Jan 30, 2026
Fast to start. Clear chapters. Great on open-source models.
Ethan Brooks • Professor
Feb 6, 2026
The february tie-ins made it feel like it was written for right now. Huge win.
Sophia Rossi • Editor
Feb 1, 2026
A solid “read → apply today” book. Also: read vibes.
Maya Chen • UX Researcher
Jan 31, 2026
This is the rare book where I highlight a lot, but I also use the highlights. The AI projects sections feel super practical.
Omar Reyes • Data Engineer
Feb 3, 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
Jan 31, 2026
Fast to start. Clear chapters. Great on text generation.
Omar Reyes • Data Engineer
Feb 6, 2026
If you care about conceptual clarity and transfer, the february tie-ins are useful prompts for further reading.
Jules Nakamura • QA Lead
Jan 30, 2026
If you care about conceptual clarity and transfer, the 2026 tie-ins are useful prompts for further reading.
Iris Novak • Writer
Feb 2, 2026
Fast to start. Clear chapters. Great on machine learning.
Omar Reyes • Data Engineer
Feb 2, 2026
If you care about conceptual clarity and transfer, the february tie-ins are useful prompts for further reading.
Sophia Rossi • Editor
Feb 4, 2026
A solid “read → apply today” book. Also: read vibes.
Noah Kim • Indie Dev
Feb 6, 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
Jan 29, 2026
A solid “read → apply today” book. Also: read vibes.
Omar Reyes • Data Engineer
Jan 29, 2026
From a structural standpoint, the text creates a coherent ladder: definitions → examples → constraints → application. That’s why the deep learning arguments land.
Sophia Rossi • Editor
Feb 7, 2026
Practical, not preachy. Loved the LLMs examples.
Jules Nakamura • QA Lead
Feb 2, 2026
From a structural standpoint, the text creates a coherent ladder: definitions → examples → constraints → application. That’s why the AI projects arguments land.
Iris Novak • Writer
Feb 5, 2026
Practical, not preachy. Loved the AI projects examples.
Benito Silva • Analyst
Feb 2, 2026
If you enjoyed Shaders Unchained: Writing Powerful Shaders for Every Platform, this one scratches a similar itch—especially around 2026 and momentum.
Lina Ahmed • Product Manager
Feb 3, 2026
Not perfect, but very useful. The read angle kept it grounded in current problems.
Leo Sato • Automation
Feb 4, 2026
If you enjoyed Foundations of Graphics & Compute - Volume 3: Computing (Hardback), this one scratches a similar itch—especially around 2026 and momentum.
Sophia Rossi • Editor
Feb 5, 2026
Practical, not preachy. Loved the LLMs examples.
Noah Kim • Indie Dev
Jan 29, 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.
Iris Novak • Writer
Feb 6, 2026
A solid “read → apply today” book. Also: trailer vibes.
Benito Silva • Analyst
Feb 1, 2026
If you enjoyed Shaders Unchained: Writing Powerful Shaders for Every Platform, this one scratches a similar itch—especially around february and momentum.
Lina Ahmed • Product Manager
Feb 5, 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 3, 2026
If you enjoyed Data Mining and Machine Learning Essentials, this one scratches a similar itch—especially around week and momentum.
Samira Khan • Founder
Feb 3, 2026
It pairs nicely with what’s trending around read—you finish a chapter and think: “okay, I can do something with this.”
Omar Reyes • Data Engineer
Feb 7, 2026
If you care about conceptual clarity and transfer, the week tie-ins are useful prompts for further reading.
Maya Chen • UX Researcher
Feb 2, 2026
It pairs nicely with what’s trending around making—you finish a chapter and think: “okay, I can do something with this.” (Side note: if you like Shaders Unchained: Writing Powerful Shaders for Every Platform, you’ll likely enjoy this too.)
Sophia Rossi • Editor
Jan 28, 2026
A solid “read → apply today” book. Also: making vibes.
Jules Nakamura • QA Lead
Feb 2, 2026
The book rewards re-reading. On pass two, the Diffusion models connections become more explicit and surprisingly rigorous.
Iris Novak • Writer
Feb 5, 2026
Fast to start. Clear chapters. Great on transformers.
Benito Silva • Analyst
Feb 7, 2026
A friend asked what I learned and I could actually explain it—because the transformers chapter is built for recall.
Sophia Rossi • Editor
Feb 3, 2026
A solid “read → apply today” book. Also: trailer vibes.
Noah Kim • Indie Dev
Jan 31, 2026
If you enjoyed Foundations of Graphics & Compute - Volume 3: Computing (Hardback), this one scratches a similar itch—especially around week and momentum.
Samira Khan • Founder
Jan 30, 2026
This is the rare book where I highlight a lot, but I also use the highlights. The ChatGPT sections feel super practical.
Theo Grant • Security
Feb 4, 2026
If you enjoyed Data Mining and Machine Learning Essentials, this one scratches a similar itch—especially around february and momentum.
Samira Khan • Founder
Jan 31, 2026
This is the rare book where I highlight a lot, but I also use the highlights. The LLMs sections feel super practical.
Omar Reyes • Data Engineer
Feb 6, 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 1, 2026
A solid “read → apply today” book. Also: making vibes.
Jules Nakamura • QA Lead
Feb 2, 2026
From a structural standpoint, the text creates a coherent ladder: definitions → examples → constraints → application. That’s why the ChatGPT arguments land.
Sophia Rossi • Editor
Jan 29, 2026
A solid “read → apply today” book. Also: trailer vibes.
Noah Kim • Indie Dev
Feb 3, 2026
If you enjoyed Shaders Unchained: Writing Powerful Shaders for Every Platform, this one scratches a similar itch—especially around february and momentum.
Nia Walker • Teacher
Feb 6, 2026
Fast to start. Clear chapters. Great on open-source models. (Side note: if you like Shaders Unchained: Writing Powerful Shaders for Every Platform, you’ll likely enjoy this too.)
Benito Silva • Analyst
Feb 4, 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 3, 2026
A solid “read → apply today” book. Also: trailer vibes.
Noah Kim • Indie Dev
Feb 1, 2026
A friend asked what I learned and I could actually explain it—because the machine learning chapter is built for recall.
Nia Walker • Teacher
Jan 29, 2026
Fast to start. Clear chapters. Great on Diffusion models.
Benito Silva • Analyst
Feb 4, 2026
A friend asked what I learned and I could actually explain it—because the transformers chapter is built for recall.
Lina Ahmed • Product Manager
Feb 3, 2026
What surprised me: the advice doesn’t collapse under real constraints. The Generative AI sections feel field-tested.
Jules Nakamura • QA Lead
Jan 29, 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 4, 2026
A solid “read → apply today” book. Also: read vibes.
Zoe Martin • Designer
Jan 31, 2026
This is the rare book where I highlight a lot, but I also use the highlights. The ChatGPT sections feel super practical.
Theo Grant • Security
Feb 1, 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.
Maya Chen • UX Researcher
Feb 4, 2026
This is the rare book where I highlight a lot, but I also use the highlights. The ChatGPT sections feel super practical.
Ethan Brooks • Professor
Jan 29, 2026
I’ve already recommended it twice. The machine learning chapter alone is worth the price.
Zoe Martin • Designer
Feb 5, 2026
It pairs nicely with what’s trending around making—you finish a chapter and think: “okay, I can do something with this.”
Harper Quinn • Librarian
Feb 6, 2026
The book rewards re-reading. On pass two, the transformers connections become more explicit and surprisingly rigorous.
Ava Patel • Student
Feb 7, 2026
Fast to start. Clear chapters. Great on Diffusion models.
Jules Nakamura • QA Lead
Feb 1, 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 1, 2026
This is the rare book where I highlight a lot, but I also use the highlights. The LLMs sections feel super practical.
Omar Reyes • Data Engineer
Feb 1, 2026
The book rewards re-reading. On pass two, the text generation connections become more explicit and surprisingly rigorous.
Sophia Rossi • Editor
Jan 29, 2026
Practical, not preachy. Loved the Generative AI examples.
Benito Silva • Analyst
Feb 2, 2026
A friend asked what I learned and I could actually explain it—because the open-source models chapter is built for recall. (Side note: if you like Data Mining and Machine Learning Essentials, you’ll likely enjoy this too.)
Sophia Rossi • Editor
Feb 4, 2026
A solid “read → apply today” book. Also: trailer vibes.
Jules Nakamura • QA Lead
Feb 5, 2026
From a structural standpoint, the text creates a coherent ladder: definitions → examples → constraints → application. That’s why the ChatGPT arguments land.
Iris Novak • Writer
Feb 1, 2026
Fast to start. Clear chapters. Great on Diffusion models.
Benito Silva • Analyst
Jan 31, 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 2, 2026
Practical, not preachy. Loved the ChatGPT examples.
Noah Kim • Indie Dev
Feb 3, 2026
I read one section during a coffee break and ended up rewriting my plan for the week. The LLMs part hit that hard.
Iris Novak • Writer
Feb 3, 2026
Practical, not preachy. Loved the LLMs examples.
Benito Silva • Analyst
Feb 6, 2026
A friend asked what I learned and I could actually explain it—because the open-source models chapter is built for recall.
Lina Ahmed • Product Manager
Jan 28, 2026
Not perfect, but very useful. The making angle kept it grounded in current problems.
Ethan Brooks • Professor
Feb 1, 2026
I’ve already recommended it twice. The Diffusion models chapter alone is worth the price.
Ava Patel • Student
Feb 5, 2026
Practical, not preachy. Loved the Generative AI examples.
Jules Nakamura • QA Lead
Feb 3, 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 7, 2026
A solid “read → apply today” book. Also: trailer vibes.
Benito Silva • Analyst
Feb 6, 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 2, 2026
A solid “read → apply today” book. Also: read vibes.
Noah Kim • Indie Dev
Jan 31, 2026
I read one section during a coffee break and ended up rewriting my plan for the week. The ChatGPT part hit that hard.
Iris Novak • Writer
Feb 4, 2026
Practical, not preachy. Loved the ChatGPT examples.
Omar Reyes • Data Engineer
Feb 7, 2026
The book rewards re-reading. On pass two, the Diffusion models connections become more explicit and surprisingly rigorous.
Sophia Rossi • Editor
Jan 31, 2026
A solid “read → apply today” book. Also: making vibes.
Maya Chen • UX Researcher
Jan 30, 2026
This is the rare book where I highlight a lot, but I also use the highlights. The LLMs sections feel super practical.
Ethan Brooks • Professor
Feb 7, 2026
Okay, wow. This is one of those books that makes you want to do things. The deep learning framing is chef’s kiss.
Sophia Rossi • Editor
Jan 31, 2026
Practical, not preachy. Loved the LLMs examples.
Noah Kim • Indie Dev
Feb 2, 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
Jan 30, 2026
Fast to start. Clear chapters. Great on open-source models.
Benito Silva • Analyst
Feb 1, 2026
If you enjoyed Data Mining and Machine Learning Essentials, this one scratches a similar itch—especially around 2026 and momentum.
Lina Ahmed • Product Manager
Feb 1, 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.
Jules Nakamura • QA Lead
Feb 1, 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
Jan 29, 2026
Practical, not preachy. Loved the deep learning examples.
Omar Reyes • Data Engineer
Feb 3, 2026
From a structural standpoint, the text creates a coherent ladder: definitions → examples → constraints → application. That’s why the ChatGPT arguments land.
Sophia Rossi • Editor
Feb 6, 2026
Fast to start. Clear chapters. Great on text generation.
Noah Kim • Indie Dev
Feb 2, 2026
A friend asked what I learned and I could actually explain it—because the text generation chapter is built for recall.
Nia Walker • Teacher
Feb 7, 2026
Fast to start. Clear chapters. Great on transformers. (Side note: if you like Data Mining and Machine Learning Essentials, you’ll likely enjoy this too.)
Benito Silva • Analyst
Jan 30, 2026
A friend asked what I learned and I could actually explain it—because the text generation chapter is built for recall.
Harper Quinn • Librarian
Feb 7, 2026
If you care about conceptual clarity and transfer, the week tie-ins are useful prompts for further reading.
Maya Chen • UX Researcher
Jan 31, 2026
This is the rare book where I highlight a lot, but I also use the highlights. The Generative AI sections feel super practical.
Leo Sato • Automation
Feb 7, 2026
A friend asked what I learned and I could actually explain it—because the open-source models chapter is built for recall.
Samira Khan • Founder
Jan 31, 2026
It pairs nicely with what’s trending around trailer—you finish a chapter and think: “okay, I can do something with this.”
Lina Ahmed • Product Manager
Feb 4, 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 2, 2026
A friend asked what I learned and I could actually explain it—because the text generation chapter is built for recall.
Demo thread: varied voice, nested replies, topic-matching language. Replace with real community posts if you collect them.
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Quick answers
Themes include Generative AI, Diffusion models, ChatGPT, transformers, LLMs, plus context from 2026, read, february, trailer.
Use the Buy/View link near the cover. We also link to Goodreads search and the original source page.
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