Business Analysis for Beginners: Jump-Start Your Ba Career in Four Weeks, Elgendy Mohamed
Автор: Elgendy Mohamed Ali Название: 3D Business Analyst ISBN: 1478726407 ISBN-13(EAN): 9781478726401 Издательство: Неизвестно Цена: 45930.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book is a simple guide for anyone who wants to learn about the Agile concept and the Scrum framework by:¢ understanding the reasons behind various approaches instead of just going through do`s and don`ts and cliches, and¢ understanding the diversity and range of ideas in this domain rather than just the latest fashion.There are three types of content in this book:1. Fundamental concepts: The first and the last chapters are about the meaning and dynamics of Agile projects. They build a solid foundation that helps you learn the details on the one hand, and on the other hand, find your own way in projects.2. Frameworks: The Scrum chapter goes through all the details of this most popular framework because anyone involved in Agile projects these days needs to be familiar with it. Another necessity is Kanban, which is explored in its own chapter.3. Practices: There are chapters about Crystal, eXtreme Programming, and DSDM(R), which all use these methods to explore the most common Agile practices and techniques.
Автор: Elgendy Mohamed Название: Deep Learning for Vision Systems ISBN: 1617296198 ISBN-13(EAN): 9781617296192 Издательство: Неизвестно Рейтинг: Цена: 52790.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: 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
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