Description : "A guide to programs currently available on video in the areas of movies/entertainment, general interest/education, sports/recreation, fine arts, health/science, business/industry, children/juvenile, how-to/instruction"--T.p.
Description : Offers a collection of articles which discuss the causes, symptoms, health and psychological effects, and treatments of eating disorders, and provides a directory of facilities and programs designed to help people with these disorders.
Description : Classifier systems play a major role in machine learning and knowledge-based systems, and Ross Quinlan's work on ID3 and C4.5 is widely acknowledged to have made some of the most significant contributions to their development. This book is a complete guide to the C4.5 system as implemented in C for the UNIX environment. It contains a comprehensive guide to the system's use , the source code (about 8,800 lines), and implementation notes. C4.5 starts with large sets of cases belonging to known classes. The cases, described by any mixture of nominal and numeric properties, are scrutinized for patterns that allow the classes to be reliably discriminated. These patterns are then expressed as models, in the form of decision trees or sets of if-then rules, that can be used to classify new cases, with emphasis on making the models understandable as well as accurate. The system has been applied successfully to tasks involving tens of thousands of cases described by hundreds of properties. The book starts from simple core learning methods and shows how they can be elaborated and extended to deal with typical problems such as missing data and over hitting. Advantages and disadvantages of the C4.5 approach are discussed and illustrated with several case studies. This book should be of interest to developers of classification-based intelligent systems and to students in machine learning and expert systems courses.
Description : Learn Docker "infrastructure as code" technology to define a system for performing standard but non-trivial data tasks on medium- to large-scale data sets, using Jupyter as the master controller. It is not uncommon for a real-world data set to fail to be easily managed. The set may not fit well into access memory or may require prohibitively long processing. These are significant challenges to skilled software engineers and they can render the standard Jupyter system unusable. As a solution to this problem, Docker for Data Science proposes using Docker. You will learn how to use existing pre-compiled public images created by the major open-source technologies—Python, Jupyter, Postgres—as well as using the Dockerfile to extend these images to suit your specific purposes. The Docker-Compose technology is examined and you will learn how it can be used to build a linked system with Python churning data behind the scenes and Jupyter managing these background tasks. Best practices in using existing images are explored as well as developing your own images to deploy state-of-the-art machine learning and optimization algorithms. What You'll Learn Master interactive development using the Jupyter platform Run and build Docker containers from scratch and from publicly available open-source images Write infrastructure as code using the docker-compose tool and its docker-compose.yml file type Deploy a multi-service data science application across a cloud-based system Who This Book Is For Data scientists, machine learning engineers, artificial intelligence researchers, Kagglers, and software developers
Description : AMERICA’S #1 BESTSELLING TELEVISION BOOK WITH MORE THAN HALF A MILLION COPIES IN PRINT– NOW REVISED AND UPDATED! PROGRAMS FROM ALL SEVEN COMMERCIAL BROADCAST NETWORKS, MORE THAN ONE HUNDRED CABLE NETWORKS, PLUS ALL MAJOR SYNDICATED SHOWS! This is the must-have book for TV viewers in the new millennium–the entire history of primetime programs in one convenient volume. It’s a guide you’ll turn to again and again for information on every series ever telecast. There are entries for all the great shows, from evergreens like The Honeymooners, All in the Family, and Happy Days to modern classics like 24, The Office, and Desperate Housewives; all the gripping sci-fi series, from Captain Video and the new Battle Star Galactica to all versions of Star Trek; the popular serials, from Peyton Place and Dallas to Dawson’s Creek and Ugly Betty; the reality show phenomena American Idol, Survivor, and The Amazing Race; and the hits on cable, including The Daily Show with Jon Stewart, Top Chef, The Sopranos, Curb Your Enthusiasm, Project Runway, and SpongeBob SquarePants. This comprehensive guide lists every program alphabetically and includes a complete broadcast history, cast, and engaging plot summary–along with exciting behind-the-scenes stories about the shows and the stars. MORE THAN 500 ALL-NEW LISTINGS from Heroes and Grey’s Anatomy to 30 Rock and Nip/Tuck UPDATES ON CONTINUING SHOWS such as CSI, Gilmore Girls, The Simpsons, and The Real World EXTENSIVE CABLE COVERAGE with more than 1,000 entries, including a description of the programming on each major cable network AND DON’T MISS the exclusive and updated “Ph.D. Trivia Quiz” of 200 questions that will challenge even the most ardent TV fan, plus a streamlined guide to TV-related websites for those who want to be constantly up-to-date SPECIAL FEATURES! • Annual program schedules at a glance for the past 61 years • Top-rated shows of each season • Emmy Award winners • Longest-running series • Spin-off series • Theme songs • A fascinating history of TV “This is the Guinness Book of World Records . . . the Encyclopedia Britannica of television!” –TV Guide
Description : Practical OpenCV is a hands-on project book that shows you how to get the best results from OpenCV, the open-source computer vision library. Computer vision is key to technologies like object recognition, shape detection, and depth estimation. OpenCV is an open-source library with over 2500 algorithms that you can use to do all of these, as well as track moving objects, extract 3D models, and overlay augmented reality. It's used by major companies like Google (in its autonomous car), Intel, and Sony; and it is the backbone of the Robot Operating System’s computer vision capability. In short, if you're working with computer vision at all, you need to know OpenCV. With Practical OpenCV, you'll be able to: Get OpenCV up and running on Windows or Linux. Use OpenCV to control the camera board and run vision algorithms on Raspberry Pi. Understand what goes on behind the scenes in computer vision applications like object detection, image stitching, filtering, stereo vision, and more. Code complex computer vision projects for your class/hobby/robot/job, many of which can execute in real time on off-the-shelf processors. Combine different modules that you develop to create your own interactive computer vision app. What you’ll learn The ins and outs of OpenCV programming on Windows and Linux Transforming and filtering images Detecting corners, edges, lines, and circles in images and video Detecting pre-trained objects in images and video Making panoramas by stitching images together Getting depth information by using stereo cameras Basic machine learning techniques BONUS: Learn how to run OpenCV on Raspberry Pi Who this book is for This book is for programmers and makers with little or no previous exposure to computer vision. Some proficiency with C++ is required. Table of ContentsPart 1: Getting comfortable Chapter 1: Introduction to Computer Vision and OpenCV Chapter 2: Setting up OpenCV on your computer Chapter 3: CV Bling – OpenCV inbuilt demos Chapter 4: Basic operations on images and GUI windows Part 2: Advanced computer vision problems and coding them in OpenCV Chapter 5: Image filtering Chapter 6: Shapes in images Chapter 7: Image segmentation and histograms Chapter 8: Basic machine learning and keypoint-based object detection Chapter 9: Affine and Perspective transformations and their applications to image panoramas Chapter 10: 3D geometry and stereo vision Chapter 11: Embedded computer vision: Running OpenCV programs on the Raspberry Pi