The Vision Code

Noted CEOs, academics, and military personnel share their experience as visionary leaders The Vision Code explores the concept of "vision" and leadership. The book reveals the secrets of building and executing a strong vision within any organization. Oleg Konovalov—an acclaimed global thought leader—draws together in one volume in-depth interviews with nineteen extraordinary global visionaries that represent a variety of industries and organizations. These leaders explain why a vision is needed, how to implement it, how to communicate a vision effectively, and how to live by it with integrity. As Konovalov explains, vision is a key leadership skill that can be developed as a practical business tool for leading a company today and into the future. The stories of the nineteen leaders reveal how to develop a compelling vision and follow through with the vision in order to inspire an entire workforce. When a leader taps into the power of "vision," he or she creates a more meaningful business experience and ultimately, a better life. This compelling book: Offers a guide for making the concept of vision a reality Provides the information needed to develop a clear and persuasive vision Contains an accessible guide to a much-needed skill Includes interviews with Marshall Goldsmith (#1 Leadership Thinker), Martin Lindstrom (#1 Branding Expert), Garry Ridge (Chairman and CEO of WD-40 Company) and many others Written for leaders at all levels in organizations and industries of any type The Vision Code is a must-have book for anyone who wants to develop the skill to become a visionary leader.

In the second book of the action-packed Double Vision series, ordinary twelve-year-old Linc Baker must go undercover in Washington, DC, to stop a CIA mole from assassinating the president. With an unforgettably funny voice, high-stakes espionage, and real American spy history dating back to the Revolutionary War, this is the latest adventure in the Double Vision series that ALA Booklist says "fans of Alex Rider and 39 Clues will love." After the government receives a threat on the president's life from someone named Dagger, supersecret spy agency Pandora wants Linc to go to Washington, DC, to protect the first daughter, Amy, and find the dangerous double of George Washington's legendary coat, rumored to make its wearer invincible. Unfortunately for Linc, his by-the-book agent nemesis, Ben Green, is already on the case and making him look bad. With Amy's help, Linc will have to dig deep into the history of America's first spies—the Culper Ring—to beat Ben at his own game and stop Dagger before it's too late.

Strategies for transforming a toxic church culture Why is it that the best strategic plans and good leadership often are not able to move churches in the desired direction? Sam Chand contends that toxic culture is to blame. Quite often, leaders don't sense the toxicity, but it poisons their relationships and derails their vision. This work describes five easily identifiable categories of church culture (inspiring-accepting-stagnant-discouraging-toxic), with diagnostic descriptions in the book and a separate online assessment tool. The reader will be able to identify strengths and needs of their church's culture, and then apply practical strategies (communication, control and authority, selection and placement of personnel, etc.) to make their church's culture more positive. Discusses how to diagnose the state of a church's culture Reveals what it takes to put in place effective strategies for creating a more positive church culture Author served on the board of EQUIP (Dr. John Maxwell's Ministry), equipping five million leaders world-wide. This important book offers a clear guide for understanding and recreating a healthy church culture.

Even bad code can function. But if code isn’t clean, it can bring a development organization to its knees. Every year, countless hours and significant resources are lost because of poorly written code. But it doesn’t have to be that way. Noted software expert Robert C. Martin presents a revolutionary paradigm with Clean Code: A Handbook of Agile Software Craftsmanship. Martin has teamed up with his colleagues from Object Mentor to distill their best agile practice of cleaning code “on the fly” into a book that will instill within you the values of a software craftsman and make you a better programmer–but only if you work at it. What kind of work will you be doing? You’ll be reading code–lots of code. And you will be challenged to think about what’s right about that code, and what’s wrong with it. More importantly, you will be challenged to reassess your professional values and your commitment to your craft. Clean Code is divided into three parts. The first describes the principles, patterns, and practices of writing clean code. The second part consists of several case studies of increasing complexity. Each case study is an exercise in cleaning up code–of transforming a code base that has some problems into one that is sound and efficient. The third part is the payoff: a single chapter containing a list of heuristics and “smells” gathered while creating the case studies. The result is a knowledge base that describes the way we think when we write, read, and clean code. Readers will come away from this book understanding How to tell the difference between good and bad code How to write good code and how to transform bad code into good code How to create good names, good functions, good objects, and good classes How to format code for maximum readability How to implement complete error handling without obscuring code logic How to unit test and practice test-driven development This book is a must for any developer, software engineer, project manager, team lead, or systems analyst with an interest in producing better code.

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

"National Council of Teachers of Mathematics."

This book introduces the geometry of 3-D vision, that is, the reconstruction of 3-D models of objects from a collection of 2-D images. It details the classic theory of two view geometry and shows that a more proper tool for studying the geometry of multiple views is the so-called rank consideration of the multiple view matrix. It also develops practical reconstruction algorithms and discusses possible extensions of the theory.

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