Vision Language Models Shivashankar Chari, Dr. Ayushman Gupta
- Autorzy:
- Shivashankar Chari, Dr. Ayushman Gupta
- Wydawnictwo:
- BPB Publications
- Ocena:
- Stron:
- 402
- Dostępne formaty:
-
ePubMobi
Opis
książki
:
Vision Language Models
Description
Enterprise AI is moving beyond text to systems that can understand images, documents, video, and language together. Vision-language models make this possible, but most real-world problems are not solved by models alone. They require well-designed pipelines, data flows, and integration with business systems.
This book focuses on building those systems end-to-end. It covers the foundations of visual and language representations and moves into modern multimodal models. From there, it discusses visual reasoning, visual question answering, and multimodal retrieval. You will work with real code using frameworks such as PyTorch, Hugging Face, OpenCV, YOLO, and learn how to combine vision encoders and retrieval layers into a single workflow. As the book progresses, it focuses on full system design. You will build pipelines that handle data ingestion, pre-processing, model inference, and output generation. The book also covers practical aspects such as model optimization, quantization, and efficient deployment to reduce latency and cost. It explains how to integrate these components into enterprise use cases, evaluate performance, and make systems reliable and scalable.
By the end of this book, you will be able to design and implement production-ready vision-language solutions that solve real business problems. What you will learn
Understand core concepts behind vision and language representations.
Implement custom self-attention and cross-attention in PyTorch.
Evaluate image captions and VQA benchmarks using CIDEr and CLIPScore.
Develop visual question answering and multimodal retrieval systems.
Apply model optimization techniques including quantization for efficiency.
Design scalable and reliable enterprise-ready AI system architectures. Who this book is for
This book is intended for machine learning engineers, data scientists, AI engineers, and software developers who want to build multimodal AI systems using vision and language models. It is also relevant for architects and technical leaders designing enterprise AI solutions. Readers are expected to have a basic understanding of Python and machine learning concepts. Familiarity with deep learning frameworks will help in implementing the examples and understanding system design decisions across real-world applications. Table of Contents
1. Introduction to Vision-language Models
2. Deep Learning and Attention Mechanisms
3. Foundations of Computer Vision for VLMs
4. Architecture of Vision-language Models
5. Dataset Designs, Evaluation Metrics and Benchmarks for VLMs
6. Pneumonia Detection from Chest X-rays
7. Object Detection and Classification with Bounding Boxes
8. Real-World Project 3 Wind Turbine Blade Damage Detection
9. Efficient Tuning, Transfer Learning, Alignment and Optimization
10. Quantization, Distillation and Efficient Inference
11. Vision-language Applications Across Domains
12. Challenges, Ethics and Road Ahead
Enterprise AI is moving beyond text to systems that can understand images, documents, video, and language together. Vision-language models make this possible, but most real-world problems are not solved by models alone. They require well-designed pipelines, data flows, and integration with business systems.
This book focuses on building those systems end-to-end. It covers the foundations of visual and language representations and moves into modern multimodal models. From there, it discusses visual reasoning, visual question answering, and multimodal retrieval. You will work with real code using frameworks such as PyTorch, Hugging Face, OpenCV, YOLO, and learn how to combine vision encoders and retrieval layers into a single workflow. As the book progresses, it focuses on full system design. You will build pipelines that handle data ingestion, pre-processing, model inference, and output generation. The book also covers practical aspects such as model optimization, quantization, and efficient deployment to reduce latency and cost. It explains how to integrate these components into enterprise use cases, evaluate performance, and make systems reliable and scalable.
By the end of this book, you will be able to design and implement production-ready vision-language solutions that solve real business problems. What you will learn
Understand core concepts behind vision and language representations.
Implement custom self-attention and cross-attention in PyTorch.
Evaluate image captions and VQA benchmarks using CIDEr and CLIPScore.
Develop visual question answering and multimodal retrieval systems.
Apply model optimization techniques including quantization for efficiency.
Design scalable and reliable enterprise-ready AI system architectures. Who this book is for
This book is intended for machine learning engineers, data scientists, AI engineers, and software developers who want to build multimodal AI systems using vision and language models. It is also relevant for architects and technical leaders designing enterprise AI solutions. Readers are expected to have a basic understanding of Python and machine learning concepts. Familiarity with deep learning frameworks will help in implementing the examples and understanding system design decisions across real-world applications. Table of Contents
1. Introduction to Vision-language Models
2. Deep Learning and Attention Mechanisms
3. Foundations of Computer Vision for VLMs
4. Architecture of Vision-language Models
5. Dataset Designs, Evaluation Metrics and Benchmarks for VLMs
6. Pneumonia Detection from Chest X-rays
7. Object Detection and Classification with Bounding Boxes
8. Real-World Project 3 Wind Turbine Blade Damage Detection
9. Efficient Tuning, Transfer Learning, Alignment and Optimization
10. Quantization, Distillation and Efficient Inference
11. Vision-language Applications Across Domains
12. Challenges, Ethics and Road Ahead
Szczegóły książki
- ISBN Ebooka:
- 978-81-696-1912-7, 9788169619127
- Data wydania ebooka :
-
2026-10-27
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- Język publikacji:
- angielski
- Rozmiar pliku ePub:
- 19.6MB
- Rozmiar pliku Mobi:
- 19.6MB
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