[1/2] Hands-On Large Language Models (AI column)

On my vacation, I brought along this visual guide to large language models, or simply LLM:) Since I perceive information visually much better than by hearing, as well as constructing schemes for visualizing complex topics, I found this book brilliant – it does not just explain complex topics, it shows them through visualizations and code.

The book was written by two ML/AI experts: Jay Alammar is an engineering fellow at Cohere, author of iconic visual guides on ML and NLP. His charts are parsed in NumPy documentation, pandas and deeplearning.ai courses. He has his own. blog Maarten Grotendorst - Data Scientist, author of open-source libraries (BERTopic, KeyBERT)Specialist in the topic of thematic modeling and embedding. He has his own. blog

They stick to a philosophy of learning when intuition first develops. (through a qualitative understanding of concepts)And then back it up with formal descriptions and examples. For this purpose, a visual narrative format is used: the book contains almost 300 original illustrations and diagrams created specifically for this edition. Through visual diagrams, graphs and drawings, complex LLM mechanisms (For example, the mechanism of self-attention in transformers, the work of tokenizers, multidimensional spaces of embeddings, etc.) They are explained gradually and easily. In addition to visualization, the authors emphasize the practical side of using language models. (This is what hands-on says in the title of the book.). They give many examples of code, scripts and practical cases. (code available GitHub).

This book is perfect for everyone. Beginners and advanced professionals in ML/NLP Developers and analysts implementing LLM projects Anyone who wants to confidently navigate modern models like ChatGPT, Mistral or Claude

In continuation A little bit about the contents of all three parts of the book.

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