Description:

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 Farre, Andres Marafioti, and Orr Zohar. 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.