# Transformers for NLP and Computer Vision, 3rd Edition

Transformers for Natural Language Processing and Computer Vision, Third Edition, is a 728-page technical guide to transformer models, LLMs, and multimodal AI. It covers model architecture, pretraining and fine-tuning, RAG, risk mitigation, and computer-vision applications, with examples involving Hugging Face, OpenAI, and Google Vertex AI.

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- Language: en-US
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- Price: 23.25 USD
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- Discount: 58%
- Price checked: 2026-11-14T05:50:12.189Z
- Rating: 4.1 / 5
- Reviews: 83

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## At a glance

- Explains transformer architectures including BERT, GPT, T5, PaLM, ViT, CLIP, and DALL-E.
- Covers pretraining and fine-tuning, tokenization, and practical model-development examples.
- Includes RAG and embedding-based search for question answering and mitigating hallucinations.
- Adds computer-vision and multimodal topics, including image and video classification.

## Pros

- Combines NLP and computer-vision coverage with hands-on topics spanning training, RAG, and model interpretation.
- Includes discussion of risks such as hallucinations, memorization, and privacy alongside mitigation approaches.
- The listing says Python familiarity and basic machine-learning concepts are helpful, while also describing UI, prompt-engineering, and no-code examples.

## Cons

- The breadth across NLP, LLMs, and computer vision may be less focused than a book dedicated to one of those areas.

## Specifications

### Publication
- Edition: 3rd
- Publisher: Packt Publishing
- Publication date: February 29, 2024
- Language: English
- Print length: 728 pages
- ISBN-13: 978-1805128724
### Dimensions and installation
- Weight listed: 2.72 pounds
- Dimensions: 7.5 x 1.65 x 9.25 inches

## Before you buy

- The listing says familiarity with Python and basic machine learning helps readers follow the use cases and code examples. It also presents some UI, prompt-engineering, and no-code examples as accessible to curious readers.

## A good fit for

- NLP and computer-vision engineers seeking a wide-ranging reference on transformers and generative AI.
- Data scientists, machine-learning practitioners, and software developers who want examples involving model training, RAG, and multimodal applications.
- Technical leaders comparing platforms and models, with foundational Python or machine-learning knowledge useful for the code examples.

## Less suited to

- Readers seeking a narrowly focused introduction to only one model family or one AI application area.
- Readers who want a book with no code or no reliance on prior Python and machine-learning familiarity.

## Customer feedback

Customer feedback describes the book as a useful resource and appreciates explanations that break complex material into digestible sections. Reports on conciseness are mixed, so readers who prefer a compact, tightly edited treatment may find the book's broad coverage less suited to that preference. That breadth is reflected in the listed 728-page length and its coverage of NLP, LLMs, RAG, and computer vision.
- Positive themes: Customers describe the book as a useful resource and praise explanations that make complex topics digestible.
- Positive themes: Comprehensiveness is a reported positive aspect.
- Mixed feedback: Feedback on conciseness is mixed, with some readers finding complex material easy to grasp while others feel it could be more concise.

## Frequently asked questions

### What subjects does this book cover?
It covers transformer architectures, LLM pretraining and fine-tuning, RAG, risk mitigation, and vision and multimodal applications.
### Does it include practical examples?
The listing describes hands-on work with model training, embedding-based search, RAG, image and video classification, and tools for visualizing model activity.
### What background is useful?
The listing says Python and basic machine-learning knowledge help with its use cases and code examples. It also describes some interface, prompt-engineering, and no-code material.
### Which edition is this?
The listing identifies it as the third edition, published February 29, 2024.

## Alternative options

- [Transformers for Natural Language Processing and Computer Vision: Specify, verify, and defend production LLM, agentic, RAG, MCP, and multimodal AI systems](https://best-deals.pro/visit/transformers-natural-language-processing-computer-vision-third-edition)
- [Build a Reasoning Model \(From Scratch\)](https://best-deals.pro/visit/transformers-natural-language-processing-computer-vision-third-edition)
- [Transformers: The Definitive Guide: Applications Beyond NLP](https://best-deals.pro/visit/transformers-natural-language-processing-computer-vision-third-edition)
- [Deep Learning: Foundations and Concepts](https://best-deals.pro/visit/transformers-natural-language-processing-computer-vision-third-edition)
- [The Lean AI Handbook: Building, Deploying, and Scaling AI for Business Value](https://best-deals.pro/visit/transformers-natural-language-processing-computer-vision-third-edition)
- [Context by Design for LLMs: A Practical Guide to Prompts, Memory, Retrieval, Tool Use, Context Windows, and Reliable AI Systems](https://best-deals.pro/visit/transformers-natural-language-processing-computer-vision-third-edition)
- [Generative AI Foundations: Build, Fine-Tune, and Deploy Generative Models for Text, Images, and Multimodal Applications](https://best-deals.pro/visit/transformers-natural-language-processing-computer-vision-third-edition)
- [AI Evals in Practice: Testing, reliability, and quality for LLM systems in production](https://best-deals.pro/visit/transformers-natural-language-processing-computer-vision-third-edition)
- [The Practical Network Engineer's Guide: Master routing, network diagnostics, and troubleshooting](https://best-deals.pro/visit/practical-network-engineers-guide-routing-troubleshooting)
- [The Agentic AI Bible: The Complete and Up-to-Date Guide to Design, Develop, and Scale Goal-Driven, LLM-Powered Agents that Think, Execute and Evolve](https://best-deals.pro/visit/transformers-natural-language-processing-computer-vision-third-edition)
- [Modern Computer Vision with PyTorch: A practical roadmap from deep learning fundamentals to advanced applications and Generative AI](https://best-deals.pro/visit/transformers-natural-language-processing-computer-vision-third-edition)
- [Mastering PyTorch: Create and deploy deep learning models from CNNs to multimodal models, LLMs, and beyond](https://best-deals.pro/visit/mastering-pytorch-second-edition-deep-learning)

## Add-ons

- [Natural Language Processing with Transformers, Revised Edition](https://best-deals.pro/visit/transformers-natural-language-processing-computer-vision-third-edition)
- [Build a Large Language Model \(From Scratch\)](https://best-deals.pro/visit/transformers-natural-language-processing-computer-vision-third-edition)

## Images

![Transformers for NLP and Computer Vision, 3rd Edition — 1](https://best-deals.pro/i/GZ_46lLWeGXeQab2M_NshGNPRMEggPhqLra8MWwo6JVnGqzhJDto0lxZ51rAdXfkcDumpoVLNx0A0aa1o-AOAwaua81Vw9vmYnBjfoyhg4Qzk7t_FRYe.jpg)