A free, world-class education for anyone, anywhere: this is the goal of the Khan Academy, a passion project that grew from an ex-engineer and hedge funder's online tutoring sessions with his niece, who was struggling with algebra, into a worldwide phenomenon. Today millions of students, parents, and teachers use the Khan Academy's free videos and software, which have expanded to encompass nearly every conceivable subject; and Academy techniques are being employed with exciting results in a growing number of classrooms around the globe.
Like many innovators, Khan rethinks existing assumptions and imagines what education could be if freed from them. And his core idea-liberating teachers from lecturing and state-mandated calendars and opening up class time for truly human interaction-has become his life's passion. Schools seek his advice about connecting to students in a digital age, and people of all ages and backgrounds flock to the site to utilize this fresh approach to learning.
In THE ONE WORLD SCHOOLHOUSE, Khan presents his radical vision for the future of education, as well as his own remarkable story, for the first time. In these pages, you will discover, among other things:
How both students and teachers are being bound by a broken top-down model invented in Prussia two centuries agoWhy technology will make classrooms more human and teachers more importantHow and why we can afford to pay educators the same as other professionalsHow we can bring creativity and true human interactivity back to learningWhy we should be very optimistic about the future of learning.Parents and politicians routinely bemoan the state of our education system. Statistics suggest we've fallen behind the rest of the world in literacy, math, and sciences. With a shrewd reading of history, Khan explains how this crisis presented itself, and why a return to "mastery learning," abandoned in the twentieth century and ingeniously revived by tools like the Khan Academy, could offer the best opportunity to level the playing field, and to give all of our children a world-class education now.
More than just a solution, THE ONE WORLD SCHOOLHOUSE serves as a call for free, universal, global education, and an explanation of how Khan's simple yet revolutionary thinking can help achieve this inspiring goal.

In the past few years, with the evolution of advanced persistent threats and mutation techniques, sensitive and damaging information from a variety of sources have been exposed to possible corruption and hacking. Machine learning, artificial intelligence, predictive analytics, and similar disciplines of cognitive science applications have been found to have significant applications in the domain of cyber security. Machine Learning and Cognitive Science Applications in Cyber Security examines different applications of cognition that can be used to detect threats and analyze data to capture malware. Highlighting such topics as anomaly detection, intelligent platforms, and triangle scheme, this publication is designed for IT specialists, computer engineers, researchers, academicians, and industry professionals interested in the impact of machine learning in cyber security and the methodologies that can help improve the performance and reliability of machine learning applications.

Computer vision has become increasingly important and effective in recent years due to its wide-ranging applications in areas as diverse as smart surveillance and monitoring, health and medicine, sports and recreation, robotics, drones, and self-driving cars. Visual recognition tasks, such as image classification, localization, and detection, are the core building blocks of many of these applications, and recent developments in Convolutional Neural Networks (CNNs) have led to outstanding performance in these state-of-the-art visual recognition tasks and systems. As a result, CNNs now form the crux of deep learning algorithms in computer vision. This self-contained guide will benefit those who seek to both understand the theory behind CNNs and to gain hands-on experience on the application of CNNs in computer vision. It provides a comprehensive introduction to CNNs starting with the essential concepts behind neural networks: training, regularization, and optimization of CNNs. The book also discusses a wide range of loss functions, network layers, and popular CNN architectures, reviews the different techniques for the evaluation of CNNs, and presents some popular CNN tools and libraries that are commonly used in computer vision. Further, this text describes and discusses case studies that are related to the application of CNN in computer vision, including image classification, object detection, semantic segmentation, scene understanding, and image generation. This book is ideal for undergraduate and graduate students, as no prior background knowledge in the field is required to follow the material, as well as new researchers, developers, engineers, and practitioners who are interested in gaining a quick understanding of CNN models.

In the past few years, with the evolution of advanced persistent threats and mutation techniques, sensitive and damaging information from a variety of sources have been exposed to possible corruption and hacking. Machine learning, artificial intelligence, predictive analytics, and similar disciplines of cognitive science applications have been found to have significant applications in the domain of cyber security. Machine Learning and Cognitive Science Applications in Cyber Security examines different applications of cognition that can be used to detect threats and analyze data to capture malware. Highlighting such topics as anomaly detection, intelligent platforms, and triangle scheme, this publication is designed for IT specialists, computer engineers, researchers, academicians, and industry professionals interested in the impact of machine learning in cyber security and the methodologies that can help improve the performance and reliability of machine learning applications.

This thesis explores the dimensions and causes of social income poverty in Rural Pakistan, focusing on nutrition and health. The analysis also tests the 'efficiency-wage' hypothesis which links better health to better income via higher productivity. The results show that serious nutritional and health poverty exists within the sample. Other key findings are: firstly, the need for subsidised food and nutrient support programmes is emphasised; next, the potential of food prices as policy tools to improve health and nutrition emerges repeatedly; and thirdly, human capital (in particular own education and mother's schooling), as well as village facilities such as health care, sanitation, and water supply, are consistently observed to boost nutritional and health status of both children and adults. The existence of an 'efficiency-wage' nexus is also supported by the findings. In general, the need for investment in human capital and social infrastructure emerges strongly, with clear intrinsic benefits, but also due to the spinoffs it generates in terms of higher potential levels of productivity, and hence overall economic development.