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Whether you’re a software engineer aspiring to enter the world of deep learning, a veteran data scientist, or a hobbyist with a simple dream of making the next viral AI app, you might have wondered where to begin. This step-by-step guide teaches you how to build practical deep learning applications for the cloud, mobile, browsers, and edge devices using a hands-on approach. Relying on years of industry experience transforming deep learning research into award-winning applications, Anirudh Koul, Siddha Ganju, and Meher Kasam guide you through the process of converting an idea into something that people in the real world can use. Train, tune, and deploy computer vision models with Keras, TensorFlow, Core ML, and TensorFlow Lite Develop AI for a range of devices including Raspberry Pi, Jetson Nano, and Google Coral Explore fun projects, from Silicon Valley’s Not Hotdog app to 40+ industry case studies Simulate an autonomous car in a video game environment and build a miniature version with reinforcement learning Use transfer learning to train models in minutes Discover 50+ practical tips for maximizing model accuracy and speed, debugging, and scaling to millions of users
What is Reverse Image Search Reverse image search is a content-based image retrieval (CBIR) query technique that involves providing the CBIR system with a sample image that it will then base its search upon; in terms of information retrieval, the sample image is very useful. In particular, reverse image search is characterized by a lack of search terms. This effectively removes the need for a user to guess at keywords or terms that may or may not return a correct result. Reverse image search also allows users to discover content that is related to a specific sample image or the popularity of an image, and to discover manipulated versions and derivative works. How you will benefit (I) Insights, and validations about the following topics: Chapter 1: Reverse image search Chapter 2: Web crawler Chapter 3: Image retrieval Chapter 4: Recommender system Chapter 5: Document retrieval Chapter 6: Content-based image retrieval Chapter 7: Automatic image annotation Chapter 8: Inverted index Chapter 9: Google Images Chapter 10: Social search (II) Answering the public top questions about reverse image search. (III) Real world examples for the usage of reverse image search in many fields. Who this book is for Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of Reverse Image Search.
What is Image Retrieval An image retrieval system is a computer system used for browsing, searching and retrieving images from a large database of digital images. Most traditional and common methods of image retrieval utilize some method of adding metadata such as captioning, keywords, title or descriptions to the images so that retrieval can be performed over the annotation words. Manual image annotation is time-consuming, laborious and expensive; to address this, there has been a large amount of research done on automatic image annotation. Additionally, the increase in social web applications and the semantic web have inspired the development of several web-based image annotation tools. How you will benefit (I) Insights, and validations about the following topics: Chapter 1: Image retrieval Chapter 2: Information retrieval Chapter 3: Content-based image retrieval Chapter 4: Automatic image annotation Chapter 5: Google Images Chapter 6: Image meta-search Chapter 7: Visual search engine Chapter 8: Reverse image search Chapter 9: TinEye Chapter 10: Image collection exploration (II) Answering the public top questions about image retrieval. (III) Real world examples for the usage of image retrieval in many fields. Who this book is for Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of Image Retrieval.
Alexander, Paige Allocca, Cinzia Anderson, Kari L. Aschehoug, Daisy P. Asinari, Neva Ault, Jill Averinos, Melissa Barbagallo, Teresa Barbin, Linda Barsness, Rachel Bearden, Nathalie Beebe, Mickey Bencsko, Michelle Engel Bermingham, Wendy Berrill, Hayley Berryhill, Andrea Bird, Bev Black, Heather Blakesley, Katie Boenish, Anna Bond, Sarah Borger, Susan Boudreaux, Mathew Bowman, Angela Box-McCoy, Kristyn Brand, Jenna Brickey, Cheryl Brown, Jessica Bryan, Rebecca Burnett, Rebecca Butler, Amy Caggiano, Arianna Callahan, Megan Camalick, Chelsea Chahley, Leanne Christ, Joan Cier, Emily Cifaldi-Morrill, Sheri Coffey, Emily Coffey, Miriam Cohen, Leanne Cole, Pamela J. Converse, Carson Corcoran, Amber Corry, Melissa Costa, Ruth Craft, Violet Crow, Nancy Dackson, Elizabeth Daksiewicz, Nicole Dandekar, Shruti Daniels, Rosalind Darby, Ben Daum, Kristy Davis, Michelle Day, Leah Deise, Alexis Deister, Anne Dithmer, Katherine Doane, Emily Doering, Shawna Dorr, Rachael Duling, Karen Dunn, Charlayne Eichler-Messmer, Kim Elliott, Libs Elliott, Heidi Evans, Season Faughnan, Tara Ferguson, Heather Ferrill James, Donna Findlay Wolfe, Victoria Fleckenstein, Krista Flower, Lysa Frieden, Wendy Friedlander, Carolyn Friend, Amy Fuchs, Yvonne Gee's Bend Gering, Jacquie Gold, Penny Gold, Lesley Goodwin, Hillary Gregory, Mary Greuter, Yara Griffin, Scott D. Grotrian, Carole Anne Haight Carlton, Alissa Hannon, Shelly Harp, Charlene Harrell, Phoebe Hartman, Elizabeth Hartrich, Laura Harvatine, Liz Harvey Lee, Karen Haynes, Luke Heinrich, Lee Heisler, Carol Heitland, Brigitte Henderson, Shea Henderson, Angie Hennebury, Krista Hertzer, Katrina Hohnstreiter, Amanda Hone Murdock, Kamie Hubbard, Solidia Hungerford, Linda Hutchinson, Rossie Ireland Beaver, Cassandra Jalbert, Debra L. Jenkins, Jeannie Jenkins, Lee Johnston, Jennifer Jones, Faith Jones, Kat Jones Rossotti, Jennifer Jubie, Becca June, Agatha Keahey, Carla Kehnle, Nydia Kerr, Bill Kerr, Mary Khaja, Samarra Kight, Kim Kimber, Chawne Kloke, Jennifer Knauer, Thomas Kyle, Susan Lang, Lauren Larson, Katie Leray, Melissa Levin, Tami Lichner, Alyssa Loewenberg, Marsha Lyon, Jenny MacDonald, Susan Maple, Karen Maroon, Nikki Marston, Gwen McDowell Hopper, Laura Mehling, Dena Menardi, Riane Menzer, Mary Miller Curley, Melissa Molen, Colleen Myer, Darby Neblett, Nicole Neill, Lindsey Nichols, Sheri O'Malley, Stacey Lee Olszewski, Bernie Orth, Lou Page, Shannon Pagliai, Shelly Paquette, Suzanne Parkes, Heidi Parson, Emily Patel, Krishma Pedersen, Katie Perrigo, Christine Perrino, Barbara Pettway, Mary Ann Pina, Gina Poplin, Elaine Wick Porcella, Yvonne Pukstas, Laura Purvis, Nancy Quilts, Quantum Ramsey Keasler, Mary Rapp, Katie Reeves, Olan Reiter, Michelle Ricks, Christine Ringle, Weeks Roach, Rebecca Rocco, Pam Roth, Wendy Rouse, Daniel Ruyle, Stephanie Ryan, Kristi Saafir, Latifah Samborski, Annette Sanclaria, Judy Santistevan, Susan Schmidt, Denyse Schraw, Sarah Schroeder, Kristi Schwarz, Dorie Seitz, Sarah Sessions, Emily Sharman, Stacey Sheridan, Caro Shibley, Beth Shields, Kristin Sipes, Lisa Skardal, Steph Skumanich, Shelby Slusser Clay, Susan Smith, Juli Irene Soper, Kim Sorenson, Jen Soto, Maritza Sovey, Corinne Sparkles, Molli Spiridon, Linda Stead, Lindsay Strong, Susan Struckmeyer, Amy Sullivan, Anne Sutters, Silvia Toye, Jessica Tuazon, Melanie Upitis, Kathryn Vandeyar, Diana Varner, Marla Vinegrad, Betsy Vojtechovsky, Kari Volckening, Bill Wade, Amy Walker, Lucinda Walters, Angela Watson, Christa Wayne, Dena Wells, Jean Whittington, Nancy Wikander, Carrie Wilkie, Michelle Williams, Suzy Williams, Julia Wilson, Sarah Withers, Krista Wood, Kelly Wood, Sherri Lynn Workman, Mary York, Kathy Young, Jaime
A concise and accessible guide to techniques for detecting doctored and fake images in photographs and digital media. Stalin, Mao, Hitler, Mussolini, and other dictators routinely doctored photographs so that the images aligned with their messages. They erased people who were there, added people who were not, and manipulated backgrounds. They knew if they changed the visual record, they could change history. Once, altering images required hours in the darkroom; today, it can be done with a keyboard and mouse. Because photographs are so easily faked, fake photos are everywhere—supermarket tabloids, fashion magazines, political ads, and social media. How can we tell if an image is real or false? In this volume in the MIT Press Essential Knowledge series, Hany Farid offers a concise and accessible guide to techniques for detecting doctored and fake images in photographs and digital media. Farid, an expert in photo forensics, has spent two decades developing techniques for authenticating digital images. These techniques model the entire image-creation process in order to find the digital disruption introduced by manipulation of the image. Each section of the book describes a different technique for analyzing an image, beginning with those requiring minimal technical expertise and advancing to those at intermediate and higher levels. There are techniques for, among other things, reverse image searches, metadata analysis, finding image imperfections introduced by JPEG compression, image cloning, tracing pixel patterns, and detecting images that are computer generated. In each section, Farid describes the techniques, explains when they should be applied, and offers examples of image analysis.
Fully-updated for Python 3, the second edition of this worldwide bestseller (over 100,000 copies sold) explores the stealthier side of programming and brings you all new strategies for your hacking projects. When it comes to creating powerful and effective hacking tools, Python is the language of choice for most security analysts. In Black Hat Python, 2nd Edition, you’ll explore the darker side of Python’s capabilities—writing network sniffers, stealing email credentials, brute forcing directories, crafting mutation fuzzers, infecting virtual machines, creating stealthy trojans, and more. The second edition of this bestselling hacking book contains code updated for the latest version of Python 3, as well as new techniques that reflect current industry best practices. You’ll also find expanded explanations of Python libraries such as ctypes, struct, lxml, and BeautifulSoup, and dig deeper into strategies, from splitting bytes to leveraging computer-vision libraries, that you can apply to future hacking projects. You’ll learn how to: • Create a trojan command-and-control using GitHub • Detect sandboxing and automate common malware tasks, like keylogging and screenshotting • Escalate Windows privileges with creative process control • Use offensive memory forensics tricks to retrieve password hashes and inject shellcode into a virtual machine • Extend the popular Burp Suite web-hacking tool • Abuse Windows COM automation to perform a man-in-the-browser attack • Exfiltrate data from a network most sneakily When it comes to offensive security, your ability to create powerful tools on the fly is indispensable. Learn how with the second edition of Black Hat Python. New to this edition: All Python code has been updated to cover Python 3 and includes updated libraries used in current Python applications. Additionally, there are more in-depth explanations of the code and the programming techniques have been updated to current, common tactics. Examples of new material that you'll learn include how to sniff network traffic, evade anti-virus software, brute-force web applications, and set up a command-and-control (C2) system using GitHub.
Showcases the best of the worst handicraft, in categories such as décor, pet humiliation, and Christmas. Based on the blog of the same name.
The classic work on the evaluation of city form. What does the city's form actually mean to the people who live there? What can the city planner do to make the city's image more vivid and memorable to the city dweller? To answer these questions, Mr. Lynch, supported by studies of Los Angeles, Boston, and Jersey City, formulates a new criterion—imageability—and shows its potential value as a guide for the building and rebuilding of cities. The wide scope of this study leads to an original and vital method for the evaluation of city form. The architect, the planner, and certainly the city dweller will all want to read this book.
What is Content Based Image Retrieval Content-based image retrieval, also known as query by image content and content-based visual information retrieval (CBVIR), is the application of computer vision techniques to the problem of image retrieval, which is the difficulty of searching for digital images in big databases. Other names for this technique include content-based visual information retriev. In contrast to the conventional concept-based methods, content-based picture retrieval is a more recent development. How you will benefit (I) Insights, and validations about the following topics: Chapter 1: Content-based image retrieval Chapter 2: Information retrieval Chapter 3: Image retrieval Chapter 4: Automatic image annotation Chapter 5: Tag cloud Chapter 6: Video search engine Chapter 7: Image organizer Chapter 8: Image meta search Chapter 9: Reverse image search Chapter 10: Visual search engine (II) Answering the public top questions about content based image retrieval. (III) Real world examples for the usage of content based image retrieval in many fields. Who this book is for Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of Content Based Image Retrieval.
David Horvitz's Sad, Depressed, People looks at a set of images circulating within stock photography collections. These photographs, in which actors are photographed holding their heads in their hands, ostensibly depressed, are here shown to contain a bizarre tension between their status as stock images and their supposedly emotional content.