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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Transformers and Large Language Models: A Hands-On Guide to Rag and Agentic AI

Transformers and Large Language Models: A Hands-On Guide to Rag and Agentic AI

Gad, Ahmed Fawzy

Paperback

General ComputersProbability & Statistics

Publisher Price: $59.99

ISBN13: 9798868827846
Publisher: Apress
Published: Jul 1 2026
Pages: 348
Weight: 1.39
Height: 0.75 Width: 7.00 Depth: 10.00
Language: English
This book is a hands-on guide to understanding the foundations, architectures, and real-world applications of transformers and large language models in modern AI.

The book begins by laying the foundations of generative AI architectures, tokenization, encoding, and classical modeling techniques. Initial chapters address the evolution from feed-forward networks and recurrent neural networks to long short-term memory (LSTM), setting the stage for the revolutionary transformer architecture. The core of the book focuses on transformers, introducing the encoder-decoder framework, attention mechanisms, positional encodings, and the internal workings of multi-head attention, normalization, and multi-layer perceptrons. Readers gain insight into advanced techniques such as rotary positional embeddings (RoPE), mixture of experts (MoE), and knowledge distillation, alongside practical training strategies like self-supervised learning, fine-tuning, and reinforcement learning with human feedback. Popular models from OpenAI, DeepSeek, and other vendors are examined to highlight the evolution of the LLM landscape. Building on these foundations, the text explores methods for model customization, including parameter-efficient fine-tuning (LoRA, adapters), text generation strategies, prompt engineering, and quantization. Retrieval-Augmented Generation (RAG) is introduced as a critical innovation for grounding LLMs in external knowledge, with detailed evaluation techniques for retrieval and generation. Finally, the book ventures into Agentic AI, demonstrating protocols like Model Context Protocol (MCP) and Agent-to-Agent (A2A) interactions with practical coding examples.

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