Best Computer Vision Books

Modern Computer Vision with PyTorch – Second Edition: A practical roadmap from deep learning fundamentals to advanced applications and Generative AI

The definitive computer vision book is back, featuring the latest neural network architectures and an exploration of foundation and diffusion models

Are you looking for a computer vision book that blends in-depth theoretical insights with practical applications? This comprehensive guide takes you on a learning journey, starting with the fundamentals of neural networks using PyTorch and advancing to cutting-edge Generative AI with a clear and progressive roadmap. This book is tailored for beginners and seasoned professionals and is an essential toolkit that empowers you to code and create innovative applications. Featuring over 40 real-world computer vision applications, along with detailed coding examples and expert advice, you’ll not only bring your ideas to life but also significantly broaden your expertise in computer vision.

Whether you are a beginner or are looking to progress in your computer vision career, this book guides you through the fundamentals of neural networks (NNs) and PyTorch and how to implement state-of-the-art architectures for real-world tasks.

The second edition of Modern Computer Vision with PyTorch is fully updated to explain and provide practical examples of the latest multimodal models, CLIP, and Stable Diffusion.

You’ll discover best practices for working with images, tweaking hyperparameters, and moving models into production. As you progress, you’ll implement various use cases for facial keypoint recognition, multi-object detection, segmentation, and human pose detection. This book provides a solid foundation in image generation as you explore different GAN architectures. You’ll leverage transformer-based architectures like ViT, TrOCR, BLIP2, and LayoutLM to perform various real-world tasks and build a diffusion model from scratch. Additionally, you’ll utilize foundation models’ capabilities to perform zero-shot object detection and image segmentation. Finally, you’ll learn best practices for deploying a model to production.

By the end of this deep learning book, you’ll confidently leverage modern NN architectures to solve real-world computer vision problems.

What you will learn

• Get to grips with various transformer-based architectures for computer vision, CLIP, Segment-Anything, and Stable Diffusion, and test their applications, such as in-painting and pose transfer
• Combine CV with NLP to perform OCR, key-value extraction from document images, visual question-answering, and generative AI tasks
• Implement multi-object detection and segmentation
• Leverage foundation models to perform object detection and segmentation without any training data points
• Learn best practices for moving a model to production

This book is for beginners to PyTorch and intermediate-level machine learning practitioners who want to learn computer vision techniques using deep learning and PyTorch. It’s useful for those just getting started with neural networks, as it will enable readers to learn from real-world use cases accompanied by notebooks on GitHub. Basic knowledge of the Python programming language and ML is all you need to get started with this book. For more experienced computer vision scientists, this book takes you through more advanced models in the latter part of the book.

• Artificial Neural Network Fundamentals
• PyTorch Fundamentals
• Building a Deep Neural Network with PyTorch
• Introducing Convolutional Neural Networks
• Transfer Learning for Image Classification
• Practical Aspects of Image Classification
• Basics of Object Detection
• Advanced Object Detection
• Image Segmentation
• Applications of Object Detection and Segmentation
• Autoencoders and Image Manipulation
• Image Generation Using GANs

Foundations of Computer Vision (Adaptive Computation and Machine Learning series)

An accessible, authoritative, and up-to-date computer vision textbook offering a comprehensive introduction to the foundations of the field that incorporates the latest deep learning advances.

Machine learning has revolutionized computer vision, but the methods of today have deep roots in the history of the field. Providing a much-needed modern treatment, this accessible and up-to-date textbook comprehensively introduces the foundations of computer vision while incorporating the latest deep learning advances.

Taking a holistic approach that goes beyond machine learning, it addresses fundamental issues in the task of vision and the relationship of machine vision to human perception. Foundations of Computer Vision covers topics not standard in other texts, including transformers, diffusion models, statistical image models, issues of fairness and ethics, and the research process

.To emphasize intuitive learning, concepts are presented in short, lucid chapters alongside extensive illustrations, questions, and examples. Written by leaders in the field and honed by a decade of classroom experience, this engaging and highly teachable book offers an essential next-generation view of computer vision.

• Up-to-date treatment integrates classic computer vision and deep learning
• Accessible approach emphasizes fundamentals and assumes little background knowledge
• Student-friendly presentation features extensive examples and images
• Proven in the classroom
• Instructor resources include slides, solutions, and source code

Practical Machine Learning for Computer Vision: End-to-End Machine Learning for Images

This practical book shows you how to employ machine learning models to extract information from images. ML engineers and data scientists will learn how to solve a variety of image problems including classification, object detection, autoencoders, image generation, counting, and captioning with proven ML techniques. This book provides a great introduction to end-to-end deep learning: dataset creation, data preprocessing, model design, model training, evaluation, deployment, and interpretability.

We recommend that you read this book in order. Make sure to read, understand, and run the accompanying notebooks in the book’s GitHub repository—you can run them in either Google Colab or Google Cloud’s Vertex Notebooks. We suggest that after reading each section of the text you try out the code to be sure you fully understand the concepts and techniques that are introduced. We strongly recommend completing the notebooks in each chapter before moving on to the next chapter.

The more complex models and larger datasets of Chapters 3, 4, 11, and 12 will benefit from the use of Google Cloud TPUs. Because all the code in this book is written using open source APIs, the code should also work in any other Jupyter environment where you have the latest version of TensorFlow installed, whether it’s your laptop, or Amazon Web Services (AWS) Sagemaker, or Azure ML. However, we haven’t tested it in those environments. If you find that you have to make any changes to get the code to work in some other environment, please do submit a pull request in order to help other readers.

Google engineers Valliappa Lakshmanan, Martin Görner, and Ryan Gillard show you how to develop accurate and explainable computer vision ML models and put them into large-scale production using robust ML architecture in a flexible and maintainable way. You’ll learn how to design, train, evaluate, and predict with models written in TensorFlow or Keras.

You’ll learn how to:

• Design ML architecture for computer vision tasks
• Select a model (such as ResNet, SqueezeNet, or EfficientNet) appropriate to your task
• Create an end-to-end ML pipeline to train, evaluate, deploy, and explain your model
• Preprocess images for data augmentation and to support learnability
• Incorporate explainability and responsible AI best practices
• Deploy image models as web services or on edge devices
• Monitor and manage ML models

Computer Vision: Algorithms and Applications (Texts in Computer Science)

Computer Vision: Algorithms and Applications explores the variety of techniques used to analyze and interpret images. It also describes challenging real-world applications where vision is being successfully used, both in specialized applications such as image search and autonomous navigation, as well as for fun, consumer-level tasks that students can apply to their own personal photos and videos.

More than just a source of “recipes,” this exceptionally authoritative and comprehensive textbook/reference takes a scientific approach to the formulation of computer vision problems. These problems are then analyzed using the latest classical and deep learning models and solved using rigorous engineering principles.

Topics and features:

• Structured to support active curricula and project-oriented courses, with tips in the Introduction for using the book in a variety of customized courses
• Incorporates totally new material on deep learning and applications such as mobile computational photography, autonomous navigation, and augmented reality
• Presents exercises at the end of each chapter with a heavy emphasis on testing algorithms and containing numerous suggestions for small mid-term projects
• Includes 1,500 new citations and 200 new figures that cover the tremendous developments from the last decade
• Provides additional material and more detailed mathematical topics in the Appendices, which cover linear algebra, numerical techniques, estimation theory, datasets, and software

Suitable for an upper-level undergraduate or graduate-level course in computer science or engineering, this textbook focuses on basic techniques that work under real-world conditions and encourages students to push their creative boundaries. Its design and exposition also make it eminently suitable as a unique reference to the fundamental techniques and current research literature in computer vision.

Transformers for Natural Language Processing and Computer Vision: Explore Generative AI and Large Language Models with Hugging Face, ChatGPT, GPT-4V, and DALL-E 3

The definitive guide to LLMs, from architectures, pretraining, and fine-tuning to Retrieval Augmented Generation (RAG), multimodal Generative AI, risks, and implementations with ChatGPT Plus with GPT-4, Hugging Face, and Vertex AI

Transformers for Natural Language Processing and Computer Vision, Third Edition, explores Large Language Model (LLM) architectures, applications, and various platforms (Hugging Face, OpenAI, and Google Vertex AI) used for Natural Language Processing (NLP) and Computer Vision (CV).

The book guides you through different transformer architectures to the latest Foundation Models and Generative AI. You’ll pretrain and fine-tune LLMs and work through different use cases, from summarization to implementing question-answering systems with embedding-based search techniques. You will also learn the risks of LLMs, from hallucinations and memorization to privacy, and how to mitigate such risks using moderation models with rule and knowledge bases. You’ll implement Retrieval Augmented Generation (RAG) with LLMs to improve the accuracy of your models and gain greater control over LLM outputs.

Dive into generative vision transformers and multimodal model architectures and build applications, such as image and video-to-text classifiers. Go further by combining different models and platforms and learning about AI agent replication.

This book provides you with an understanding of transformer architectures, pretraining, fine-tuning, LLM use cases, and best practices.

This book is ideal for NLP and CV engineers, software developers, data scientists, machine learning engineers, and technical leaders looking to advance their LLMs and generative AI skills or explore the latest trends in the field.

Knowledge of Python and machine learning concepts is required to fully understand the use cases and code examples. However, with examples using LLM user interfaces, prompt engineering, and no-code model building, this book is great for anyone curious about the AI revolution.

• Breakdown and understand the architectures of the Original Transformer, BERT, GPT models, T5, PaLM, ViT, CLIP, and DALL-E
• Fine-tune BERT, GPT, and PaLM 2 models
• Learn about different tokenizers and the best practices for preprocessing language data
• Pretrain a RoBERTa model from scratch
• Implement retrieval augmented generation and rules bases to mitigate hallucinations
• Visualize transformer model activity for deeper insights using BertViz, LIME, and SHAP
• Go in-depth into vision transformers with CLIP, DALL-E 2, DALL-E 3, and GPT-4V

Computer Vision: Principles, Algorithms, Applications, Learning

Computer Vision: Principles, Algorithms, Applications, Learning (previously entitled Computer and Machine Vision) clearly and systematically presents the basic methodology of computer vision, covering the essential elements of the theory while emphasizing algorithmic and practical design constraints. This fully revised fifth edition has brought in more of the concepts and applications of computer vision, making it a very comprehensive and up-to-date text suitable for undergraduate and graduate students, researchers and R&D engineers working in this vibrant subject.

• Three new chapters on Machine Learning emphasise the way the subject has been developing; Two chapters cover Basic Classification Concepts and Probabilistic Models; and the The third covers the principles of Deep Learning Networks and shows their impact on computer vision, reflected in a new chapter Face Detection and Recognition.
• A new chapter on Object Segmentation and Shape Models reflects the methodology of machine learning and gives practical demonstrations of its application.
• In-depth discussions have been included on geometric transformations, the EM algorithm, boosting, semantic segmentation, face frontalisation, RNNs and other key topics.
• Examples and applications―including the location of biscuits, foreign bodies, faces, eyes, road lanes, surveillance, vehicles and pedestrians―give the ‘ins and outs’ of developing real-world vision systems, showing the realities of practical implementation.
• Necessary mathematics and essential theory are made approachable by careful explanations and well-illustrated examples.
• The ‘recent developments’ sections included in each chapter aim to bring students and practitioners up to date with this fast-moving subject.
• Tailored programming examples―code, methods, illustrations, tasks, hints and solutions (mainly involving MATLAB and C++)

Computer Vision: Exploring Algorithms, Architectures, and Real-World Applications

Computer Vision: Exploring Algorithms, Architectures, and Real-World Applications is your comprehensive guide to understanding the dynamic field of computer vision. This book demystifies complex concepts, providing a clear and accessible pathway from foundational principles to cutting-edge applications.

Dive into the world of algorithms and architectures that power visual perception in machines. You’ll explore essential topics such as image processing, feature extraction, and machine learning techniques, with a focus on deep learning frameworks like CNNs and advanced models. Each chapter is filled with practical examples, hands-on projects, and detailed explanations that reinforce your understanding.

From object detection and segmentation to real-world applications in healthcare, automotive, and robotics, this book highlights the transformative impact of computer vision across various industries. Additionally, ethical considerations and future trends are discussed, preparing you for the challenges and opportunities ahead.

This book equips you with the knowledge and tools to harness the power of computer vision in your projects and innovations. Join us on this journey to explore the future of visual intelligence!