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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.
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