Tabla de Contenidos: “…Cover -- Contents -- Foreword -- Introduction -- CHAPTER ONE: It's About Vision -- Chasing Vision -- CHAPTER TWO: Within the Frame -- Shoot What Moves You -- Make Me Care -- Putting It in the Frame -- Excluding It from the Frame -- Indecisive Moments -- It's All Subjective -- The Illusion of the Exotic -- Putting Time Within the Frame -- Length of Time -- Choice of Moment -- CHAPTER THREE: The Artist and the Geek -- Gear Is Good, Vision Is Better -- Decent Exposures -- The Best Digital Negative -- Seeing the Light -- Choose Your Lens Based on Behavior -- Telephoto Lenses -- Pushing the Wide -- The Rule of Thirds -- There Is No Un-Suck Filter -- Inspiration and Perspiration -- CHAPTER FOUR: Storytelling -- Universal Themes -- Conflict Within the Frame -- The Photo Essay -- Relationships -- Attention Management -- Leaving Clues and Provoking Questions -- CHAPTER FIVE: Photographing People -- Approaching People -- The Language Barrier -- The Eyes Have It -- Capturing Emotion -- On Giving and Taking -- Indecent Exposures -- Photographing Children -- Photographing the Elderly -- Candids vs. …”

Tabla de Contenidos: “…Intro -- Table of Contents -- About the Authors -- About the Technical Reviewer -- Introduction -- Chapter 1: The Building Blocks of Computer Vision -- What Is Computer Vision -- Applications -- Classification -- Object Detection and Localization -- Image Segmentation -- Anomaly Detection -- Video Analysis -- Channels -- Convolutional Neural Networks -- Receptive Field -- Local Receptive Field -- Global Receptive Field -- Pooling -- Max Pooling -- Average Pooling -- Global Average Pooling -- Calculation: Feature Map and Receptive Fields -- Kernel -- Stride -- Pooling -- Padding -- Input and Output -- Calculation of Receptive Field -- Understanding the CNN Architecture Type -- Understanding Types of Architecture -- AlexNet -- VGG -- ResNet -- Inception Architectures -- Working with Deep Learning Model Techniques -- Batch Normalization -- Dropouts -- Data Augmentation Techniques -- Introduction to PyTorch -- Installation -- Basic Start -- Summary -- Chapter 2: Image Classification -- Topics to Cover -- Defining the Problem -- Overview of the Approach -- Creating an Image Classification Pipeline -- First Basic Model -- Data -- Data Exploration -- Data Loader -- Define the Model -- The Training Process -- The Second Variation of Model -- The Third Variation of the Model -- The Fourth Variation of the Model -- Summary -- Chapter 3: Building an Object Detection Model -- Object Detection Using Boosted Cascade -- R-CNN -- The Region Proposal Network -- Fast Region-Based Convolutional Neural Network -- How the Region Proposal Network Works -- The Anchor Generation Layer -- The Region Proposal Layer -- Mask R-CNN -- Prerequisites -- YOLO -- YOLO V2/V3 -- Project Code Snippets -- Step 1: Getting Annotated Data -- Step 2: Fixing the Configuration File and Training -- The Model File -- Summary -- Chapter 4: Building an Image Segmentation Model…”

Tabla de Contenidos: “…Front Cover; Mathematical Optimization in Computer Graphics and Vision; Copyright Page; Table of Contents; List of Figures; Preface; Acknowledgments; Chapter 1. …”

“…Available in print and digital formats. Why mission and vision are so crucial to non-profits, why so many fall short--and how to overcome this problem. …”

Materias:

Materias:

Tabla de Contenidos: “…Fundamental of robotics -- Introduction to Computer Vision -- Fundamentals of natural language processing -- Neural networks with NLP -- Convolutional neural networks for Computer Vision -- Robot operating system (ROS) -- Build a text-bases dialogue system (Chatbot) -- Object recognition to guide a robot using CNNs -- Computer Vision for robotics…”

Materias:

Materias:

Tabla de Contenidos: “…Modern Computer Vision with PyTorch: Explore deep learning concepts and implement over 50 real-world image applications…”

“…This course provides an introduction to using deep learning models to solve computer vision tasks in TensorFlow and is focused on the work horse of deep learning image models: the convolutional neural network. …”

“…Remote Vision…”