4 Books to Deepen Your Understanding of LLMs: Theory & Engineering

Author(s): Youssef Hosni

Originally published on Towards AI.

4 Books Deep Diving into Large Language Models (LLMs)

Large Language Models (LLMs) have transformed natural language processing, powering applications from chatbots to text generation and beyond.

Whether you’re looking to build an LLM from scratch, fine-tune existing models, or integrate them into real-world applications, understanding both the theoretical and engineering aspects is crucial.

This article explores four essential books that provide a deep dive into LLMs:

Build a Large Language Model (from Scratch) — A hands-on guide by Sebastian Raschka that walks you through designing, pretraining, and fine-tuning an LLM step by step.Hands-on Large Language Models — A practical resource for leveraging pretrained LLMs for text classification, retrieval-augmented generation (RAG), and multimodal applications.LLM Engineering Handbook — A deep dive into data engineering, model fine-tuning, and deploying LLMs with MLOps, covering real-time inference and optimization.AI Engineering — A comprehensive look at building AI applications with foundation models, discussing prompt engineering, dataset preparation, and model evaluation.

Whether you’re a researcher, developer, or AI enthusiast, these books provide the knowledge and tools needed to navigate the evolving landscape of LLMs and apply them effectively in various domains.

Build LLM from ScratchHands-on Large Language ModelsLLM Engineering HandbookAI Engineering

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Published via Towards AI