Best Books for Beginners to Learn About LLM (Large Language Models)
If you’re new to LLM (Large Language Models) and want to understand how they work, this reading list will help you build knowledge step by step—from basic AI concepts to hands-on implementation.
1. Understanding AI and LLM Basics
① Artificial Intelligence: A Guide for Thinking Humans – Melanie Mitchell
✅ Best for: Beginners who want to understand AI in a non-technical way
✅ Key Topics:
• Fundamental concepts of AI, including LLMs
• The history and philosophy of artificial intelligence
• The ethical implications of AI
💡 Why Read It?
Before diving into the technical aspects of LLMs, it’s important to understand the broader AI landscape. This book explains AI concepts in a clear and engaging way, without requiring a technical background.
2. Learning the Fundamentals of Machine Learning & Deep Learning
② Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow – Aurélien Géron
✅ Best for: Developers & engineers new to machine learning and deep learning
✅ Key Topics:
• Basics of machine learning and deep learning
• How neural networks work
• Implementing models with TensorFlow and Keras
💡 Why Read It?
LLMs are built on deep learning principles, so understanding neural networks and deep learning fundamentals is essential. This book offers both theoretical explanations and practical coding exercises to help beginners learn by doing.
3. Understanding NLP and Transformer Models
③ Natural Language Processing with Transformers – Lewis Tunstall, Leandro von Werra, Thomas Wolf
✅ Best for: Those who want to understand how LLMs like GPT work
✅ Key Topics:
• Introduction to Natural Language Processing (NLP)
• How transformer models (like GPT & BERT) function
• Hands-on tutorials using Hugging Face
💡 Why Read It?
LLMs rely on transformer models, so understanding how they process text is crucial. This book provides practical implementations and real-world examples using popular NLP frameworks.
4. Ethical Considerations & the Future of AI
④ The Alignment Problem: Machine Learning and Human Values – Brian Christian
✅ Best for: Anyone interested in AI ethics and the challenges of aligning AI with human values
✅ Key Topics:
• Bias in AI models, including LLMs
• The societal impact of AI
• Case studies on machine learning failures and improvements
💡 Why Read It?
As LLMs become more powerful, ethical concerns around bias, misinformation, and decision-making grow. This book explores the risks and solutions for aligning AI with human values.
5. Practical Applications of LLMs in Real-World Projects
⑤ Generative AI with Python and TensorFlow 2 – Joseph Babcock, Raghav Bali
✅ Best for: Developers & professionals looking to implement LLMs in projects
✅ Key Topics:
• How to build AI-powered applications with GPT and BERT
• Use cases in chatbots, content generation, and automation
• Implementing LLMs using TensorFlow
💡 Why Read It?
Once you understand how LLMs work, the next step is to apply them in real-world projects. This book provides practical guidance on building and deploying AI applications using LLMs.
📚 Recommended Reading Path for Beginners
🔰 Step 1: Understand AI and LLM Concepts
✔ Artificial Intelligence: A Guide for Thinking Humans
📌 Step 2: Learn Machine Learning & Deep Learning Basics
✔ Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
🧠 Step 3: Study NLP & Transformer Models
✔ Natural Language Processing with Transformers
⚖ Step 4: Explore AI Ethics and Societal Impact
✔ The Alignment Problem
🚀 Step 5: Implement LLMs in Real Projects
✔ Generative AI with Python and TensorFlow 2
Conclusion: Which Book Should You Start With?
• If you’re a non-technical reader → Start with Artificial Intelligence: A Guide for Thinking Humans
• If you want hands-on experience in ML/DL → Read Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
• If you want to understand how LLMs like GPT work → Read Natural Language Processing with Transformers
• If you’re concerned about AI ethics → Read The Alignment Problem
• If you want to build real-world LLM applications → Read Generative AI with Python and TensorFlow 2
By following this reading roadmap, you’ll gain a solid foundation in LLMs—from basic concepts to real-world applications!