A Beginner’s Guide to Generative AI [Bhati]

[mklabgr]

A Beginner’s Guide to Generative AI: An Introductory Path to Diffusion Models, ChatGPT, and LLMs
Authors: Deepshikha Bhati, Fnu Neha, Angela Guercio, Md Amiruzzaman & Aloysius Bathi Kasturiarachi
Publisher: Springer, Cham
Series:Synthesis Lectures on Computer Science
Length: XIX + 236 pages
Field: Artificial Intelligence / Machine Learning / Generative AI / NLP 

A Beginner’s Guide to Generative AI is an introductory textbook intended for readers with little or no previous background in artificial intelligence. Its aim is to explain how modern generative systems produce text and images while gradually introducing the underlying machine-learning ideas. The book begins with a general introduction to generative AI and then traces the development of language modelling from earlier neural-network approaches toward large language models (LLMs). Particular attention is given to the Transformer architecture, explaining why attention-based models became the foundation of systems such as ChatGPT. 

The middle chapters focus more closely on LLMs, Transformers, ChatGPT and Google Bard, including an architectural discussion of ChatGPT. The authors then move from language generation to image generation, introducing three major families of generative models: Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and diffusion models. The diffusion-model chapter explains their basic architecture and why diffusion techniques have become particularly effective for realistic image synthesis. 

The final part becomes more practical. There is a short chapter on setting up an environment and implementing LLM-based systems, followed by a substantial discussion of ChatGPT applications in areas such as business, customer service, marketing, content creation, healthcare and other professional settings. Chapters contain summaries and multiple-choice exercises, making the book closer to an introductory course or self-study text than to an advanced research monograph. 

Main topics

1. Introduction to Generative AI
   
2. Evolution from neural networks to large language models
   
3. Transformers and LLMs
   
4. ChatGPT architecture
   
5. Google Bard and related models
   
6. VAEs, GANs and diffusion models for image generation
   
7. Setting up an environment for LLM implementation
   
8. Practical ChatGPT use cases 
   
   

Key takeaways

• Good entry point for beginners: it assumes no substantial previous AI knowledge and builds the concepts progressively. 
  
• It provides a useful conceptual bridge from traditional neural networks → Transformers → modern LLMs rather than discussing ChatGPT only as a black-box application. 
  
• Its coverage is broader than just LLMs: it also explains the major approaches to generative image modelling, particularly VAEs, GANs and diffusion models. 
  
• It is primarily an introductory and educational overview, not a mathematically rigorous treatment of deep learning. For someone wanting to understand what generative AI systems are doing before studying the mathematics and programming in greater depth, that is probably its strongest role. 
  

BOOK