Inhaltsangabe
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), and others, written by leading researchers and practitioners Merve Noyan, Miquel Farré, Andrés 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
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Über die Autorin bzw. den Autor
Merve Noyan is a machine learning engineer working in the ML advocacy engineering team at Hugging Face. She builds tools to enable people to build with vision language models across the Hugging Face ecosystem (transformers, TRL, smolagents). Previously she worked for different companies building natural language understanding based solutions on information retrieval and conversational agents.
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Paperback. Zustand: New. 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), and others, written by leading researchers and practitioners Merve Noyan, Miquel Farre, Andres 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 techniquesTrain and fine-tune VLMs with Hugging Face, PyTorch, and othersDeploy models for applications like image search and captioningImplement advanced inference strategies, from zero-shot to agentic systemsBuild scalable VLM systems ready for production use. Bestandsnummer des Verkäufers LU-9798341624047
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Paperback. Zustand: new. Paperback. 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), and others, written by leading researchers and practitioners Merve Noyan, Miquel Farre, Andres 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 techniquesTrain and fine-tune VLMs with Hugging Face, PyTorch, and othersDeploy models for applications like image search and captioningImplement advanced inference strategies, from zero-shot to agentic systemsBuild scalable VLM systems ready for production use 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), and others, written by leading researchers and practitioners Merve Noyan, Miquel Farre, Andres Marafioti, and Orr Zohar. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Bestandsnummer des Verkäufers 9798341624047
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Paperback. Zustand: New. 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), and others, written by leading researchers and practitioners Merve Noyan, Miquel Farre, Andres 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 techniquesTrain and fine-tune VLMs with Hugging Face, PyTorch, and othersDeploy models for applications like image search and captioningImplement advanced inference strategies, from zero-shot to agentic systemsBuild scalable VLM systems ready for production use. Bestandsnummer des Verkäufers LU-9798341624047
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Paperback. Zustand: New. 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), and others, written by leading researchers and practitioners Merve Noyan, Miquel Farre, Andres 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 techniquesTrain and fine-tune VLMs with Hugging Face, PyTorch, and othersDeploy models for applications like image search and captioningImplement advanced inference strategies, from zero-shot to agentic systemsBuild scalable VLM systems ready for production use. Bestandsnummer des Verkäufers LU-9798341624047
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Paperback. Zustand: new. Paperback. 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), and others, written by leading researchers and practitioners Merve Noyan, Miquel Farre, Andres 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 techniquesTrain and fine-tune VLMs with Hugging Face, PyTorch, and othersDeploy models for applications like image search and captioningImplement advanced inference strategies, from zero-shot to agentic systemsBuild scalable VLM systems ready for production use 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), and others, written by leading researchers and practitioners Merve Noyan, Miquel Farre, Andres Marafioti, and Orr Zohar. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Bestandsnummer des Verkäufers 9798341624047
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Paperback. Zustand: New. 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), and others, written by leading researchers and practitioners Merve Noyan, Miquel Farre, Andres 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 techniquesTrain and fine-tune VLMs with Hugging Face, PyTorch, and othersDeploy models for applications like image search and captioningImplement advanced inference strategies, from zero-shot to agentic systemsBuild scalable VLM systems ready for production use. Bestandsnummer des Verkäufers LU-9798341624047
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Taschenbuch. Zustand: Neu. Neuware - 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), and others, written by leading researchers and practitioners Merve Noyan, Miquel Farré, Andrés 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. Bestandsnummer des Verkäufers 9798341624047
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