Techo Reads
101 Generative AI Projects: Diffusion Models, Transformers, ChatGPT, and Other LLMs (Paperback)
A comprehensive guide to mastering Generative AI, Diffusion models, ChatGPT and more.
Book Details
- ISBN: 9798291798089
- Publication Date: July 10, 2025
- Pages: 572
- Publisher: Tech Publications
About This Book
This book provides in-depth coverage of Generative AI and Diffusion models, offering practical insights and real-world examples that developers can apply immediately in their projects.
What You'll Learn
- Master the fundamentals of Generative AI
- Implement advanced techniques for Diffusion models
- Optimize performance in ChatGPT applications
- Apply best practices from industry experts
- Troubleshoot common issues and pitfalls
Who This Book Is For
This book is perfect for developers with intermediate experience looking to deepen their knowledge of Generative AI and Diffusion models. Whether you're building enterprise applications or working on personal projects, you'll find valuable insights and techniques.
Reviews & Discussions
The practical advice here is immediately applicable to Generative. It’s the kind of book you’ll keep on your desk, not your shelf. It’s helped me write cleaner, more maintainable code across the board.
I’ve bookmarked several chapters for quick reference on AI projects. The writing style is clear, concise, and refreshingly jargon-free.
The author's experience really shines through in their treatment of text generation.
This book completely changed my approach to text generation.
The practical advice here is immediately applicable to open-source models.
This book distilled years of confusion into a clear roadmap for Other. The troubleshooting tips alone are worth the price of admission.
The examples in this book are incredibly practical for Diffusion.
I finally feel equipped to make informed decisions about Models,. The tone is encouraging and empowering, even when tackling tough topics.
This book gave me the confidence to tackle challenges in machine learning.
I’ve bookmarked several chapters for quick reference on Diffusion models.
This helped me connect the dots I’d been missing in AI projects.
The author has a gift for explaining complex concepts about transformers. I particularly appreciated the chapter on best practices and common pitfalls. The emphasis on readability and structure has elevated our entire codebase.
This book made me rethink how I approach AI projects. I feel more confident tackling complex projects after reading this.
A must-read for anyone trying to master (Paperback).
This book made me rethink how I approach Other.
It’s the kind of book that stays relevant no matter how much you know about Projects:. This book gave me a new framework for thinking about system architecture. This book gave me the tools to finally tackle that long-standing bottleneck.
The author has a gift for explaining complex concepts about Models,. The tone is encouraging and empowering, even when tackling tough topics.
It’s the kind of book that stays relevant no matter how much you know about AI projects.
I’ve bookmarked several chapters for quick reference on ChatGPT. I’ve already recommended this to several teammates and junior devs. It’s helped me write cleaner, more maintainable code across the board.
I've read many books on this topic, but this one stands out for its clarity on Generative AI. I especially liked the real-world case studies woven throughout.
I've read many books on this topic, but this one stands out for its clarity on ChatGPT.
It’s like having a mentor walk you through the nuances of ChatGPT.
This book distilled years of confusion into a clear roadmap for open-source models.
I've been recommending this to all my colleagues working with Diffusion. I appreciated the thoughtful breakdown of common design patterns.
The insights in this book helped me solve a critical problem with text generation.
I finally feel equipped to make informed decisions about Other. I feel more confident tackling complex projects after reading this.
I wish I'd discovered this book earlier—it’s a game changer for AI projects. I appreciated the thoughtful breakdown of common design patterns. I’ve used several of the patterns described here in production already.
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