This book explores the relationship between vision and learning and the role of optometrists in the assessment and management of learning related vision problems. It discusses normal child development, the learning process, learning disabilities, the relationship between vision and learning, and models for managing vision problems affecting learning. It is also of interest to health care practitioners involved in the evaluation and treatment of children and adults with learning difficulties. Instructor resources are available; please contact your Elsevier sales representative for details. Presents an organized, easy-to-follow approach to the diagnosis and treatment of learning-related vision problems.Each chapter contains key terms and chapter review questions making it more appealing to the student and instructor.Includes appendices containing sample reports, sample questionnaires, sample letters, a bibliography, and case histories showing the reader how to use the material from the book in practice.Well respected authors and contributors provide authoritative coverage of the topic. Expanded information on the use of colored lenses and reading.New chapter on reading disorders that covers how children learn to read, teaching methods, optometric assessment, and management of dyslexia.Chapters have been updated with new computer software options, including computer aided vision therapy, perceptual home therapy system, and temporal visual processing program.Updated testing battery, including new tests, visual processing speed, and optometric use of IQ screening tests such as K-BIT.Expanded coverage of psycho education evaluation includes substantial updates with new test instruments, such as WISC.Substantial revisions based on literature review for last 10 years.New and updated illustrations.

How does the computer learn to understand what it sees? Deep Learning for Vision Systems answers that by applying deep learning to computer vision. Using only high school algebra, this book illuminates the concepts behind visual intuition. You'll understand how to use deep learning architectures to build vision system applications for image generation and facial recognition. Summary Computer vision is central to many leading-edge innovations, including self-driving cars, drones, augmented reality, facial recognition, and much, much more. Amazing new computer vision applications are developed every day, thanks to rapid advances in AI and deep learning (DL). Deep Learning for Vision Systems teaches you the concepts and tools for building intelligent, scalable computer vision systems that can identify and react to objects in images, videos, and real life. With author Mohamed Elgendy's expert instruction and illustration of real-world projects, you’ll finally grok state-of-the-art deep learning techniques, so you can build, contribute to, and lead in the exciting realm of computer vision! Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology How much has computer vision advanced? One ride in a Tesla is the only answer you’ll need. Deep learning techniques have led to exciting breakthroughs in facial recognition, interactive simulations, and medical imaging, but nothing beats seeing a car respond to real-world stimuli while speeding down the highway. About the book How does the computer learn to understand what it sees? Deep Learning for Vision Systems answers that by applying deep learning to computer vision. Using only high school algebra, this book illuminates the concepts behind visual intuition. You'll understand how to use deep learning architectures to build vision system applications for image generation and facial recognition. What's inside Image classification and object detection Advanced deep learning architectures Transfer learning and generative adversarial networks DeepDream and neural style transfer Visual embeddings and image search About the reader For intermediate Python programmers. About the author Mohamed Elgendy is the VP of Engineering at Rakuten. A seasoned AI expert, he has previously built and managed AI products at Amazon and Twilio. Table of Contents PART 1 - DEEP LEARNING FOUNDATION 1 Welcome to computer vision 2 Deep learning and neural networks 3 Convolutional neural networks 4 Structuring DL projects and hyperparameter tuning PART 2 - IMAGE CLASSIFICATION AND DETECTION 5 Advanced CNN architectures 6 Transfer learning 7 Object detection with R-CNN, SSD, and YOLO PART 3 - GENERATIVE MODELS AND VISUAL EMBEDDINGS 8 Generative adversarial networks (GANs) 9 DeepDream and neural style transfer 10 Visual embeddings

Poor vision, if unnoticed and untreated, can dramatically reduce a childOs school achievement. While it is easy to blame underachievement on a variety of causes, the real cause may be directly related to vision development. Strong vision is much more than being able to see the blackboard from the back row. Solid visual skills that underlie brain learning are developed through a variety of activities that are less and less a part of childrenOs lives thanks to TV and video games. Eyes for Learning explains how parents and teachers can spot a vision-related learning problem and how to treat it. Dr. Antonia Orfield provides answers about referrals, required vision tests, and vision-improvement techniques. The bottom line is that good vision is a learned skill that is best developed by the practices explained in this book. Understanding these explanations can go a long way in saving a child from failure in school.

A modern treatment focusing on learning and inference, with minimal prerequisites, real-world examples and implementable algorithms.

Why environmental learning is crucial for understanding the connected challenges of climate justice, tribalism, inequity, democracy, and human flourishing. How can we respond to the current planetary ecological emergency? In To Know the World, Mitchell Thomashow proposes that we revitalize, revisit, and reinvigorate how we think about our residency on Earth. First, we must understand that the major challenges of our time—migration, race, inequity, climate justice, and democracy—connect to the biosphere. Traditional environmental education has accomplished much, but it has not been able to stem the inexorable decline of global ecosystems. Thomashow, the former president of a college dedicated to sustainability, describes instead environmental learning, a term signifying that our relationship to the biosphere must be front and center in all aspects of our daily lives. In this illuminating book, he provides rationales, narratives, and approaches for doing just that. Mixing memoir, theory, mindfulness, pedagogy, and compelling storytelling, Thomashow discusses how to navigate the Anthropocene's rapid pace of change without further separating psyche from biosphere; why we should understand migration both ecologically and culturally; how to achieve constructive connectivity in both social and ecological networks; and why we should take a cosmopolitan bioregionalism perspective that unites local and global. Throughout, Thomashow invites readers to participate as educational explorers, encouraging them to better understand how and why environmental learning is crucial to human flourishing.

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. 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

Deep learning algorithms have brought a revolution to the computer vision community by introducing non-traditional and efficient solutions to several image-related problems that had long remained unsolved or partially addressed. This book presents a collection of eleven chapters where each individual chapter explains the deep learning principles of a specific topic, introduces reviews of up-to-date techniques, and presents research findings to the computer vision community. The book covers a broad scope of topics in deep learning concepts and applications such as accelerating the convolutional neural network inference on field-programmable gate arrays, fire detection in surveillance applications, face recognition, action and activity recognition, semantic segmentation for autonomous driving, aerial imagery registration, robot vision, tumor detection, and skin lesion segmentation as well as skin melanoma classification. The content of this book has been organized such that each chapter can be read independently from the others. The book is a valuable companion for researchers, for postgraduate and possibly senior undergraduate students who are taking an advanced course in related topics, and for those who are interested in deep learning with applications in computer vision, image processing, and pattern recognition.