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Digital Learning in Motion provides a theoretical analysis of learning and related learning media in society. The book explores how changing media affects learning environments, which changes the learning itself, showing that learning is always in motion. This book expounds upon the concept of learning, reconstructing how learning unfolds and analyzing the discourse around pedagogy and Bildung in the age of new digital media. It further discusses in detail the threefold relationship between learning and motion, considering how learning is based on motion, generated by new experiences and changes with the environment and through its own mediatization. The book presents a normative model that outlines how learning can be structured on the basis of society’s values and self-understanding discourses in the digital age. This book will be of great interest for academics, postgraduate students, and researchers in the fields of digital learning and inclusion, education research, educational theory, communication and cultural studies.
Knowledge in Motion brings together archaeologists, historians, and cultural anthropologists to examine communities from around the globe as they engage in a range of practices constituting situated learned and knowledge transmission. The contributors lay the groundwork to forge productive theories and methodologies for exploring situated learning and its broad-ranging outcomes.
Summary Grokking Deep Learning teaches you to build deep learning neural networks from scratch! In his engaging style, seasoned deep learning expert Andrew Trask shows you the science under the hood, so you grok for yourself every detail of training neural networks. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Deep learning, a branch of artificial intelligence, teaches computers to learn by using neural networks, technology inspired by the human brain. Online text translation, self-driving cars, personalized product recommendations, and virtual voice assistants are just a few of the exciting modern advancements possible thanks to deep learning. About the Book Grokking Deep Learning teaches you to build deep learning neural networks from scratch! In his engaging style, seasoned deep learning expert Andrew Trask shows you the science under the hood, so you grok for yourself every detail of training neural networks. Using only Python and its math-supporting library, NumPy, you'll train your own neural networks to see and understand images, translate text into different languages, and even write like Shakespeare! When you're done, you'll be fully prepared to move on to mastering deep learning frameworks. What's inside The science behind deep learning Building and training your own neural networks Privacy concepts, including federated learning Tips for continuing your pursuit of deep learning About the Reader For readers with high school-level math and intermediate programming skills. About the Author Andrew Trask is a PhD student at Oxford University and a research scientist at DeepMind. Previously, Andrew was a researcher and analytics product manager at Digital Reasoning, where he trained the world's largest artificial neural network and helped guide the analytics roadmap for the Synthesys cognitive computing platform. Table of Contents Introducing deep learning: why you should learn it Fundamental concepts: how do machines learn? Introduction to neural prediction: forward propagation Introduction to neural learning: gradient descent Learning multiple weights at a time: generalizing gradient descent Building your first deep neural network: introduction to backpropagation How to picture neural networks: in your head and on paper Learning signal and ignoring noise:introduction to regularization and batching Modeling probabilities and nonlinearities: activation functions Neural learning about edges and corners: intro to convolutional neural networks Neural networks that understand language: king - man + woman == ? Neural networks that write like Shakespeare: recurrent layers for variable-length data Introducing automatic optimization: let's build a deep learning framework Learning to write like Shakespeare: long short-term memory Deep learning on unseen data: introducing federated learning Where to go from here: a brief guide
As many as one in 20 children struggles with sensory issues. To address these needs in the classroom, sensory integration has become a staple in early education. This book is sure to be every teacher's bible of sensory activities.
"This book highlights the development of robust and effective vision-based motion understanding systems, addressing specific vision applications such as surveillance, sport event analysis, healthcare, video conferencing, and motion video indexing and retrieval"--Provided by publisher.
Plumb the depths of core motion design fundamentals and harness the essential techniques of this diverse and innovative medium. Combine basic art and design principles with creative storytelling to create compelling style frames, design boards, and motion design projects. Here, in one volume, Austin Shaw covers all the principles any serious motion designer needs to know in order to make their artistic visions a reality and confidently produce compositions for clients, including: Illustration techniques Typography Compositing Cinematography Incorporating 3D elements Matte painting Concept development, and much more Lessons are augmented by illustrious full color imagery and practical exercises, allowing you to put the techniques covered into immediate practical context. Industry leaders and pioneers, including Karin Fong, Bradley G Munkowitz (GMUNK), Will Hyde, Erin Sarofsky, Danny Yount, and many more, contribute their professional perspectives, share personal stories, and provide visual examples of their work. Additionally, a robust companion website (www.focalpress.com/cw/shaw) features project files, video tutorials, bonus PDFs, and rolling updates to keep you informed on the latest developments in the field.
An eminent psychologist offers a major new theory of human cognition: movement, not language, is the foundation of thought When we try to think about how we think, we can't help but think of words. Indeed, some have called language the stuff of thought. But pictures are remembered far better than words, and describing faces, scenes, and events defies words. Anytime you take a shortcut or play chess or basketball or rearrange your furniture in your mind, you've done something remarkable: abstract thinking without words. In Mind in Motion, psychologist Barbara Tversky shows that spatial cognition isn't just a peripheral aspect of thought, but its very foundation, enabling us to draw meaning from our bodies and their actions in the world. Our actions in real space get turned into mental actions on thought, often spouting spontaneously from our bodies as gestures. Spatial thinking underlies creating and using maps, assembling furniture, devising football strategies, designing airports, understanding the flow of people, traffic, water, and ideas. Spatial thinking even underlies the structure and meaning of language: why we say we push ideas forward or tear them apart, why we're feeling up or have grown far apart. Like Thinking, Fast and Slow before it, Mind in Motion gives us a new way to think about how--and where--thinking takes place.
Techniques of vision-based motion analysis aim to detect, track, identify, and generally understand the behavior of objects in image sequences. With the growth of video data in a wide range of applications from visual surveillance to human-machine interfaces, the ability to automatically analyze and understand object motions from video footage is of increasing importance. Among the latest developments in this field is the application of statistical machine learning algorithms for object tracking, activity modeling, and recognition. Developed from expert contributions to the first and second International Workshop on Machine Learning for Vision-Based Motion Analysis, this important text/reference highlights the latest algorithms and systems for robust and effective vision-based motion understanding from a machine learning perspective. Highlighting the benefits of collaboration between the communities of object motion understanding and machine learning, the book discusses the most active forefronts of research, including current challenges and potential future directions. Topics and features: provides a comprehensive review of the latest developments in vision-based motion analysis, presenting numerous case studies on state-of-the-art learning algorithms; examines algorithms for clustering and segmentation, and manifold learning for dynamical models; describes the theory behind mixed-state statistical models, with a focus on mixed-state Markov models that take into account spatial and temporal interaction; discusses object tracking in surveillance image streams, discriminative multiple target tracking, and guidewire tracking in fluoroscopy; explores issues of modeling for saliency detection, human gait modeling, modeling of extremely crowded scenes, and behavior modeling from video surveillance data; investigates methods for automatic recognition of gestures in Sign Language, and human action recognition from small training sets. Researchers, professional engineers, and graduate students in computer vision, pattern recognition and machine learning, will all find this text an accessible survey of machine learning techniques for vision-based motion analysis. The book will also be of interest to all who work with specific vision applications, such as surveillance, sport event analysis, healthcare, video conferencing, and motion video indexing and retrieval.
Cycling from practice to theory and back again, this concise book provides the skinny on motion leadership, or how to “move” individuals, institutions, and whole systems forward.
United States. Interdepartmental Committee on Visual and Auditory Materials for Distribution Abroad. Subcommittee on Catalog
Author : United States. Interdepartmental Committee on Visual and Auditory Materials for Distribution Abroad. Subcommittee on Catalog Publisher : Page : 480 pages File Size : 42,16 MB Release : 1959 Category : Documentary films ISBN :