Vision language models / by Merve Noyan, Miquel Farré , Andrés Marafioti , and Orr Zohar.

Format:

Book

Author/Creator:

Noyan, Merve, author.

Farré, Miquel, author.

Marafioti, Andrés, author.

Zohar, Orr, author.

Language:

English

Subjects (All):

Computer vision.

Natural language processing (Computer science).

Machine learning.

Physical Description:

1 online resource (300 pages)

Edition:

[First edition].

Place of Publication:

Sebastopol, CA : O'Reilly Media, Inc., [2026]

Summary:

Vision language models (VLMs) combine computer vision and natural language processing to create powerful systems that can interpret, generate, and respond in multimodal contexts. Vision Language Models is a hands-on guide to building real-world VLMs using the most up-to-date stack of machine learning tools from Hugging Face, Meta (PyTorch), NVIDIA (Cuda), OpenAI (CLIP), and others, written by leading researchers and practitioners Merve Noyan, Miquel Farr©♭, Andr©♭s Marafioti, and Orr Zohar. From image captioning and document understanding to advanced zero-shot inference and retrieval-augmented generation (RAG), this book covers the full VLM application and development lifecycle. Designed for ML engineers, data scientists, and developers, this guide distills cutting-edge VLM research into practical techniques. Readers will learn how to prepare datasets, select the right architectures, fine-tune and deploy models, and apply them to real-world tasks across a range of industries. Explore core model architectures and alignment techniques Train and fine-tune VLMs with Hugging Face, PyTorch, and others Deploy models for applications like image search and captioning Implement advanced inference strategies, from zero-shot to agentic systems Build scalable VLM systems ready for production use.

Notes:

OCLC-licensed vendor bibliographic record.

ISBN:

979-83-416-2403-0

OCLC:

1524017997