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The obvious Getting started
I know that most people want to study llms in depth, and although courses and articles are good for getting broad knowledge, one needs to refer to books for deeper understanding. Another thing I personally like about books is their structure. They have an accurate and coherent instruction compared to other courses that are heard around the world. With this motivation, we are starting a new series for our readers to recommend 5 free but absolutely appropriate books for different roles. So, if you take things in terms of understanding how effective linguistic models (LLMS) really are, here are my recommendations for 5 free books that you should start with him.
The obvious 1. Basics of major language models
Published in early 2025, Fundamentals of major language models it is one of the most structured and clear books written for anyone who wants to really understand how llms are built, trained, and aligned. The authors (Tong Xiao & Jiao and Jingbo Zhu) are both well known in natural language processing (NLP). Instead of rushing to every new structure or trend, they carefully explain the main methods behind modern models like GPT, Bert, and LLama.
This book emphasizes basic thinking: What pre-training means, how generated models work internally, why moving strategies is appropriate, and what “alignment” involves in good machine behavior. I think that the considered balance between theory and implementation, was designed for both students and practitioners who want to build a solid conceptual foundation before the start of the exam.
// Let's look at the framework
- Pre-training (Overview, different environment, Bert, practical aspects of synchronizing and using pre-trained models, etc.)
- Generative models (decoder-only transformers, data preparation, distributed training, scaling rules, memory tolerance, efficiency techniques, etc.)
- Recovery (principles of rapid good design, improved motivational methods, efficiency techniques)
- Alignment (llm alignment and rlhf, command setting, reward modeling, preference selection)
- Humility (guided by algorithms, evaluation metrics, effective measurement methods)
The obvious 2. Speech and Language Processing
If you want to understand NLP and LLMS in depth, Speech and Language Performance By Daniel Jurafsky and James H. Martin it is one of the best resources. The 3rd Edition Draft (August 24, 2025 Release) is completely updated to cover modern NLP, including transformers, LLMS, automatic speech recognition (Encodec & Vall-e). Jurafsky and Martin are leaders in computational languages, and their book is widely used in top universities.
It provides a clear, structured approach from basics such as tokens and embedding in advanced topics such as LLM training, alignment and interview structure. Circular PDF is freely available, making it practical and accessible.
// Let's look at the framework
- Volume I: Major models of language
- Chapters 1-2: Introduction, Words, Tokens, and Unicode Management
- Chapters 3-5: IN-GRAM LMS, Logistic regression for text classification, and vector empomdings
- Chapters 6-8: Neural Networks, LLMS, and Transforms – including sampling techniques
- Chapters 9-12: Post-training suppression, combinatorial linguistic models, IR & Rag, and machine translation
- Chapter 13: RNNS and LSSMs (Optional Selection of Sequence Learning Models)
- Chapters 14-16: Phonetics, speech feature extraction, automatic speech recognition (whispering), and text-to-god (Encodec & Vall-e)
- Volume II: The Definition of Linguistic Structure
- Chapters 17-25: Sequence labeling, POS & NER, CFGS, Dependency reduction, data extraction, semantic organization optimization, independence optimization, and dialog structure
The obvious 3. How to measure your model: View of LLMS programs in TPUS
Training LLMS can be difficult because the numbers are large, the hardware is complex, and it is difficult to know where the bottlenecks are. How to scale your model: Overview of LLMS programs at TPUS It takes an approach that is used to be more specific, to describe the side of LLMS as TSOR processing units (TPUS) work (TPUS) (and the devices that communicate with them under the hood, and how the LLMs connect, and how the LLMS actually works on real hardware. It also includes techniques for matching and training and submitting to well-sized models in large sizes.
This resource is outstanding because the authors have actually worked on LLM-level LLM programs themselves at Google, so they share their experiences.
// Let's look at the framework
- Part 0: Rooflines (Understanding hardware constraints: flops, memory Bandwidth, memory)
- Part 1: TPUS (How TPUS works with network together for multi-chip training)
- Part 2: SHARDING (Matrix replication, TPU communication costs)
- Part 3: Transformer Math (Calculating Flops, Bytes, and other sensitive metrics)
- Part 4: Training (related techniques: data parallelism, fully noisy data (FSDP), tensor parallelism, pipeline parallelism)
- Part 5: Training LLama (Practical Examples of Training Llama 3 on TPU v5p; Cost, marriage and size considerations)
- Part 6: Considerations (latency considerations, effective sampling and accelerator usage)
- Part 7: Working on LLama (working on Llama models 3-700b on TPU v5e; KV gates, batch sizes, sharding and latency estimation)
- Part 8: Profiling (functional performance using the XLA compiler and proparing tools)
- Part 9: Jax (TPus program well with jax)
The obvious 4. Understanding the major models of languages: looking for strong interpretations and references using clarifiers and self-justification
Understanding large linguistic models: Towards robust and objective interpretations using clarifiers and justifications it is not an ordinary book. It is the doctoral method of Jenny Kunz from Linköping University, but it includes a unique feature of LLMS that deserves a place on this list. He examines how large linguistic models work and how we can better understand them.
LLMS performs very well in many tasks, but it is not clear how they make their predictions. This thesis studies two ways to understand these models: Looking at the internal layers using classifiers and testing the religious models that generate their predictions. He also examines models that generate free text meanings through their predictions, examining which properties of these meanings actually help the descending tasks and adapt to human understanding. This work is useful for researchers and developers interested in building transparent systems and virtual AI literature.
// Let's look at the framework
- Understanding of the layers of LLM with the identification of Classifiers (analyzing the information stored in each part of the model, looking at the limitations of existing evaluation methods, creating complex evaluation tests using changes in known structures)
- Explaining the prediction with models of positioning (producing textual explanations and model predictions, comparing the explanations with human measurements and job performance, studying that the explanations make the explanations easier to understand and easier to understand the different tasks)
The obvious 5. Major language models in cybersecurity maturity: threats, exposure and mitigation
LLMS has great potential, but it can also create risks such as leaking private information, facilitating phishing attacks, or introducing code vulnerabilities. Major language models in cybersecurity: threats, exposure and mitigation It explains these risks and shows ways to reduce them. It includes real examples, including social engineering, monitoring LLM admissions, and setting up secure LLM systems.
This resource is unique because it focuses on LLMS in cybersecurity, a topic most LLM publications do not cover. It is very helpful for anyone who wants to understand both the risks and protections associated with LLMS.
// Let's look at the framework
- Part I: Introduction (How LLMS works and how to use them, limitations of LLMS and evaluation of their functions)
- Part II: LLMS in cybersecurity maturity (privilege engineering risks, engineering piracy and social engineering attacks, code-optimization risks, web-use index operations)
- Part III: Exposure to Follow-up and Forecasting (LLM Trends and Risks, Issues and Issues of Insurance, Corporate and Legal Issues, Looking at New Research in LLMS)
- Part IV: Mitigation (Security and Awareness, Privacy – Training Methods, Defenses Against Attacks and Exploitation, Application of LLM, Cooperation and Security)
- Part v: Conclusion (the role of llmms in identifying threats and providing protection, recommendations for safe use of LLMS)
The obvious Wrapping up
All of these books approach LLMS from very different angles: Theory, languages, systems, interpretation and security. Together, they create a complete way of learning for anyone who is most interested in learning large-scale language models. If you liked this article, let me know in the comment section below what topics you would like to explore more.
Kanwal Mehreen Is a machine learning engineer and technical writer with a strong interest in data science and the intersection of AI and medicine. Authored the eBook “Increasing Productivity with Chatgpt”. As a Google Event 2022 APAC host, she is a symbol of diversity and excellence in education. He has also been recognized as a teradata distinction in tech scholar, a mitacs Globalk research scholar, and a Harvard WeCode Scholar. Kanwal is a passionate advocate for change, who has created femcodes to empower women.
