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Deep Learning For A Digital Age
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Book Synopsis Deep Learning for a Digital Age by : Van B. Weigel
Download or read book Deep Learning for a Digital Age written by Van B. Weigel and published by Jossey-Bass. This book was released on 2001-11-12 with total page 200 pages. Available in PDF, EPUB and Kindle. Book excerpt: "In this book, Weigel shows how faculty can help students develop skills in research, problem solving, critical thinking, and knowledge management by using web-based collaboration tools. He outlines a blended, "bricks and clicks" approach to learning that emphasizes cognitive apprenticeship and communities of inquiry. Weigel's vision of "depth education" relies strongly on virtual teams and embedded assessment as a means to cultivate connection between students and educators.
Download or read book Deep Learning written by Michael Fullan and published by Corwin Press. This book was released on 2017-11-06 with total page 209 pages. Available in PDF, EPUB and Kindle. Book excerpt: New Pedagogies for Deep Learning (NDPL) provides a comprehensive strategy for systemwide transformation. Using the 6 competencies of NDPL and a wealth of vivid examples, Fullan re-defines and re-examines what deep learning is and identifies the practical strategies for revolutionizing learning and leadership.
Book Synopsis Strategies for Deep Learning with Digital Technology by : Robert Zheng
Download or read book Strategies for Deep Learning with Digital Technology written by Robert Zheng and published by . This book was released on 2018 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: How to utilize digital technology to engage learners in deep learning is an issue that warrants significant attention in 21st century education. Deep learning refers to learners engagement in critical and creative thinking, making inferences and transferring knowledge. Modern technologies like virtual reality, artificial intelligence, and 3D visualization provide the platform for deep learning in an educational setting more effectively. This book presents a collection of essays on the relationship between digital technologies and deep learning. The edited volume focuses on cognitive, metacognitive and affective processes in digital technology-based deep learning. A unique feature of the book is its emphasis on bridging the theories with practice where the practice of deep learning with digital technology is well-grounded in relevant theories and theoretical frameworks. Moreover, the book includes case studies to effectively promote the application of digital technology in deep learning. As such, the book is rightly poised to address current issues facing deep learning and digital technology in education. The audience will find this book a useful companion as they will soon discover that this book provides helpful information on both theoretical and practical aspects in deep learning with digital technology. It also serves as an excellent resource for researchers and individual professionals who seek to understand the relationship between deep learning and digital technology in education.
Download or read book Deep Learning written by Ian Goodfellow and published by MIT Press. This book was released on 2016-11-10 with total page 801 pages. Available in PDF, EPUB and Kindle. Book excerpt: An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives. “Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.” —Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.
Book Synopsis Language and Learning in the Digital Age by : James Paul Gee
Download or read book Language and Learning in the Digital Age written by James Paul Gee and published by Routledge. This book was released on 2011-02-01 with total page 168 pages. Available in PDF, EPUB and Kindle. Book excerpt: In Language and Learning in the Digital Age, linguist James Paul Gee and educator Elisabeth Hayes deal with the forces unleashed by today’s digital media, forces that are transforming language and learning for good and ill. They argue that the role of oral language is almost always entirely misunderstood in debates about digital media. Like the earlier inventions of writing and print, digital media actually power up or enhance the powers of oral language. Gee and Hayes deal, as well, with current digital transformations of language and literacy in the context of a growing crisis in traditional schooling in developed countries. With the advent of new forms of digital media, children are increasingly drawn towards video games, social media, and alternative ways of learning. Gee and Hayes explore the way in which these alternative methods of learning can be a force for a paradigm change in schooling. This is an engaging, accessible read both for undergraduate and graduate students and for scholars in language, linguistics, education, media and communication studies.
Book Synopsis Handbook of Research on Applications and Implementations of Machine Learning Techniques by : Sathiyamoorthi Velayutham
Download or read book Handbook of Research on Applications and Implementations of Machine Learning Techniques written by Sathiyamoorthi Velayutham and published by IGI Global, Engineering Science Reference. This book was released on 2019-08-23 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This book examines the practical applications and implementation of various machine learning techniques in various fields such as agriculture, medical, image processing, and networking"--
Download or read book Deep Learning written by Andrew Glassner and published by No Starch Press. This book was released on 2021-06-22 with total page 1239 pages. Available in PDF, EPUB and Kindle. Book excerpt: A richly-illustrated, full-color introduction to deep learning that offers visual and conceptual explanations instead of equations. You'll learn how to use key deep learning algorithms without the need for complex math. Ever since computers began beating us at chess, they've been getting better at a wide range of human activities, from writing songs and generating news articles to helping doctors provide healthcare. Deep learning is the source of many of these breakthroughs, and its remarkable ability to find patterns hiding in data has made it the fastest growing field in artificial intelligence (AI). Digital assistants on our phones use deep learning to understand and respond intelligently to voice commands; automotive systems use it to safely navigate road hazards; online platforms use it to deliver personalized suggestions for movies and books - the possibilities are endless. Deep Learning: A Visual Approach is for anyone who wants to understand this fascinating field in depth, but without any of the advanced math and programming usually required to grasp its internals. If you want to know how these tools work, and use them yourself, the answers are all within these pages. And, if you're ready to write your own programs, there are also plenty of supplemental Python notebooks in the accompanying Github repository to get you going. The book's conversational style, extensive color illustrations, illuminating analogies, and real-world examples expertly explain the key concepts in deep learning, including: • How text generators create novel stories and articles • How deep learning systems learn to play and win at human games • How image classification systems identify objects or people in a photo • How to think about probabilities in a way that's useful to everyday life • How to use the machine learning techniques that form the core of modern AI Intellectual adventurers of all kinds can use the powerful ideas covered in Deep Learning: A Visual Approach to build intelligent systems that help us better understand the world and everyone who lives in it. It's the future of AI, and this book allows you to fully envision it. Full Color Illustrations
Download or read book Deep Learning written by John D. Kelleher and published by MIT Press. This book was released on 2019-09-10 with total page 298 pages. Available in PDF, EPUB and Kindle. Book excerpt: An accessible introduction to the artificial intelligence technology that enables computer vision, speech recognition, machine translation, and driverless cars. Deep learning is an artificial intelligence technology that enables computer vision, speech recognition in mobile phones, machine translation, AI games, driverless cars, and other applications. When we use consumer products from Google, Microsoft, Facebook, Apple, or Baidu, we are often interacting with a deep learning system. In this volume in the MIT Press Essential Knowledge series, computer scientist John Kelleher offers an accessible and concise but comprehensive introduction to the fundamental technology at the heart of the artificial intelligence revolution. Kelleher explains that deep learning enables data-driven decisions by identifying and extracting patterns from large datasets; its ability to learn from complex data makes deep learning ideally suited to take advantage of the rapid growth in big data and computational power. Kelleher also explains some of the basic concepts in deep learning, presents a history of advances in the field, and discusses the current state of the art. He describes the most important deep learning architectures, including autoencoders, recurrent neural networks, and long short-term networks, as well as such recent developments as Generative Adversarial Networks and capsule networks. He also provides a comprehensive (and comprehensible) introduction to the two fundamental algorithms in deep learning: gradient descent and backpropagation. Finally, Kelleher considers the future of deep learning—major trends, possible developments, and significant challenges.
Book Synopsis The Deep Learning Revolution by : Terrence J. Sejnowski
Download or read book The Deep Learning Revolution written by Terrence J. Sejnowski and published by MIT Press. This book was released on 2018-10-23 with total page 354 pages. Available in PDF, EPUB and Kindle. Book excerpt: How deep learning—from Google Translate to driverless cars to personal cognitive assistants—is changing our lives and transforming every sector of the economy. The deep learning revolution has brought us driverless cars, the greatly improved Google Translate, fluent conversations with Siri and Alexa, and enormous profits from automated trading on the New York Stock Exchange. Deep learning networks can play poker better than professional poker players and defeat a world champion at Go. In this book, Terry Sejnowski explains how deep learning went from being an arcane academic field to a disruptive technology in the information economy. Sejnowski played an important role in the founding of deep learning, as one of a small group of researchers in the 1980s who challenged the prevailing logic-and-symbol based version of AI. The new version of AI Sejnowski and others developed, which became deep learning, is fueled instead by data. Deep networks learn from data in the same way that babies experience the world, starting with fresh eyes and gradually acquiring the skills needed to navigate novel environments. Learning algorithms extract information from raw data; information can be used to create knowledge; knowledge underlies understanding; understanding leads to wisdom. Someday a driverless car will know the road better than you do and drive with more skill; a deep learning network will diagnose your illness; a personal cognitive assistant will augment your puny human brain. It took nature many millions of years to evolve human intelligence; AI is on a trajectory measured in decades. Sejnowski prepares us for a deep learning future.
Book Synopsis Machine Learning with PyTorch and Scikit-Learn by : Sebastian Raschka
Download or read book Machine Learning with PyTorch and Scikit-Learn written by Sebastian Raschka and published by Packt Publishing Ltd. This book was released on 2022-02-25 with total page 775 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book of the bestselling and widely acclaimed Python Machine Learning series is a comprehensive guide to machine and deep learning using PyTorch's simple to code framework. Purchase of the print or Kindle book includes a free eBook in PDF format. Key FeaturesLearn applied machine learning with a solid foundation in theoryClear, intuitive explanations take you deep into the theory and practice of Python machine learningFully updated and expanded to cover PyTorch, transformers, XGBoost, graph neural networks, and best practicesBook Description Machine Learning with PyTorch and Scikit-Learn is a comprehensive guide to machine learning and deep learning with PyTorch. It acts as both a step-by-step tutorial and a reference you'll keep coming back to as you build your machine learning systems. Packed with clear explanations, visualizations, and examples, the book covers all the essential machine learning techniques in depth. While some books teach you only to follow instructions, with this machine learning book, we teach the principles allowing you to build models and applications for yourself. Why PyTorch? PyTorch is the Pythonic way to learn machine learning, making it easier to learn and simpler to code with. This book explains the essential parts of PyTorch and how to create models using popular libraries, such as PyTorch Lightning and PyTorch Geometric. You will also learn about generative adversarial networks (GANs) for generating new data and training intelligent agents with reinforcement learning. Finally, this new edition is expanded to cover the latest trends in deep learning, including graph neural networks and large-scale transformers used for natural language processing (NLP). This PyTorch book is your companion to machine learning with Python, whether you're a Python developer new to machine learning or want to deepen your knowledge of the latest developments. What you will learnExplore frameworks, models, and techniques for machines to 'learn' from dataUse scikit-learn for machine learning and PyTorch for deep learningTrain machine learning classifiers on images, text, and moreBuild and train neural networks, transformers, and boosting algorithmsDiscover best practices for evaluating and tuning modelsPredict continuous target outcomes using regression analysisDig deeper into textual and social media data using sentiment analysisWho this book is for If you have a good grasp of Python basics and want to start learning about machine learning and deep learning, then this is the book for you. This is an essential resource written for developers and data scientists who want to create practical machine learning and deep learning applications using scikit-learn and PyTorch. Before you get started with this book, you'll need a good understanding of calculus, as well as linear algebra.
Book Synopsis From Curiosity to Deep Learning by : Julie Coiro
Download or read book From Curiosity to Deep Learning written by Julie Coiro and published by Taylor & Francis. This book was released on 2023-10-10 with total page 386 pages. Available in PDF, EPUB and Kindle. Book excerpt: From Curiosity to Deep Learning: Personal Digital Inquiry in Grades K-5 reveals the powerful learning that results when you integrate purposeful technology into a classroom culture that values curiosity and deep learning. The centerpiece of this practical guide is Personal Digital Inquiry (PDI), a framework developed by Julie Coiro and implemented in classrooms by her co-authors, Elizabeth Dobler and Karen Pelekis. Clear, detailed examples offer ideas for K-5 teachers and school librarians to support their teaching.Personal emphasizes the significance of the personal relationship between teachers and students, and the role that students have in the learning process. Digital reflects the important role that digital texts and tools have come to play in both learning and teaching with inquiry. Inquiry lies at the core of PDI, because learners grow and change with opportunities to identify problems, generate personal wonderings, and engage in collaborative dialogue, making learning relevant and lasting.From Curiosity to Deep Learning: Personal Digital Inquiry in Grades K-5 shows you how to integrate inquiry with a range of digital tools and resources that will create a dynamic classroom for both you and your students.
Book Synopsis Deep Learning Illustrated by : Jon Krohn
Download or read book Deep Learning Illustrated written by Jon Krohn and published by Addison-Wesley Professional. This book was released on 2019-08-05 with total page 725 pages. Available in PDF, EPUB and Kindle. Book excerpt: "The authors’ clear visual style provides a comprehensive look at what’s currently possible with artificial neural networks as well as a glimpse of the magic that’s to come." – Tim Urban, author of Wait But Why Fully Practical, Insightful Guide to Modern Deep Learning Deep learning is transforming software, facilitating powerful new artificial intelligence capabilities, and driving unprecedented algorithm performance. Deep Learning Illustrated is uniquely intuitive and offers a complete introduction to the discipline’s techniques. Packed with full-color figures and easy-to-follow code, it sweeps away the complexity of building deep learning models, making the subject approachable and fun to learn. World-class instructor and practitioner Jon Krohn–with visionary content from Grant Beyleveld and beautiful illustrations by Aglaé Bassens–presents straightforward analogies to explain what deep learning is, why it has become so popular, and how it relates to other machine learning approaches. Krohn has created a practical reference and tutorial for developers, data scientists, researchers, analysts, and students who want to start applying it. He illuminates theory with hands-on Python code in accompanying Jupyter notebooks. To help you progress quickly, he focuses on the versatile deep learning library Keras to nimbly construct efficient TensorFlow models; PyTorch, the leading alternative library, is also covered. You’ll gain a pragmatic understanding of all major deep learning approaches and their uses in applications ranging from machine vision and natural language processing to image generation and game-playing algorithms. Discover what makes deep learning systems unique, and the implications for practitioners Explore new tools that make deep learning models easier to build, use, and improve Master essential theory: artificial neurons, training, optimization, convolutional nets, recurrent nets, generative adversarial networks (GANs), deep reinforcement learning, and more Walk through building interactive deep learning applications, and move forward with your own artificial intelligence projects Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.
Book Synopsis Art in the Age of Machine Learning by : Sofian Audry
Download or read book Art in the Age of Machine Learning written by Sofian Audry and published by MIT Press. This book was released on 2021-11-23 with total page 215 pages. Available in PDF, EPUB and Kindle. Book excerpt: An examination of machine learning art and its practice in new media art and music. Over the past decade, an artistic movement has emerged that draws on machine learning as both inspiration and medium. In this book, transdisciplinary artist-researcher Sofian Audry examines artistic practices at the intersection of machine learning and new media art, providing conceptual tools and historical perspectives for new media artists, musicians, composers, writers, curators, and theorists. Audry looks at works from a broad range of practices, including new media installation, robotic art, visual art, electronic music and sound, and electronic literature, connecting machine learning art to such earlier artistic practices as cybernetics art, artificial life art, and evolutionary art. Machine learning underlies computational systems that are biologically inspired, statistically driven, agent-based networked entities that program themselves. Audry explains the fundamental design of machine learning algorithmic structures in terms accessible to the nonspecialist while framing these technologies within larger historical and conceptual spaces. Audry debunks myths about machine learning art, including the ideas that machine learning can create art without artists and that machine learning will soon bring about superhuman intelligence and creativity. Audry considers learning procedures, describing how artists hijack the training process by playing with evaluative functions; discusses trainable machines and models, explaining how different types of machine learning systems enable different kinds of artistic practices; and reviews the role of data in machine learning art, showing how artists use data as a raw material to steer learning systems and arguing that machine learning allows for novel forms of algorithmic remixes.
Book Synopsis Testing in the digital age by : Tom van de Ven
Download or read book Testing in the digital age written by Tom van de Ven and published by Uitgeverij kleine Uil. This book was released on 2018-06-02 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Testing in the digital age brings a new vision on test engineering, using new quality attributes that tackle intelligent machines and a roadmap split up in fi ve hops. With everything digital there are more possibilities for test automation and piles of (test) data growing out of control. Working together with robots (cobotics), using artifi cial intelligence in testing and eventually predict the occurrence of defects brings your testing to the digital age. We have interviewed companies on their view of digital testing. A glossary brings an extensive list of terms that supports you in all your test communications.
Book Synopsis Teaching in a Digital Age by : A. W Bates
Download or read book Teaching in a Digital Age written by A. W Bates and published by . This book was released on 2015 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Learning in a Digital World by : Paloma Díaz
Download or read book Learning in a Digital World written by Paloma Díaz and published by Springer. This book was released on 2019-06-29 with total page 339 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book aims at guiding the educators from a variety of available technologies to support learning and teaching by discussing the learning benefits and the challenges that interactive technology imposes. This guidance is based on practical experiences gathered through developing and integrating them into varied educational settings. It compiles experiences gained with various interactive technologies, offering a comprehensive perspective on the use and potential value of interactive technologies to support learning and teaching. Taken together, the chapters provide a broader view that does not focus exclusively on the uses of technology in educational settings, but also on the impact and ability of technology to improve the learning and teaching processes. The book addresses the needs of researchers, educators and other stakeholders in the area of education interested in learning how interactive technologies can be used to overcome key educational challenges.
Book Synopsis The Digital Age in Agriculture by : Mehmet Metin Ozguven
Download or read book The Digital Age in Agriculture written by Mehmet Metin Ozguven and published by CRC Press. This book was released on 2023-02-23 with total page 307 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Digital Age in Agriculture presents information related to the digital age in the agriculture sector. Agriculture is an essential activity for the continuity of life, yet is very labor-intensive and faces a wide variety of challenges. In the struggle against these difficulties, the superior features offered by technology provide important benefits. These technologies require expertise in various technical disciplines, and The Digital Age in Agriculture provides information to readers allowing them to make more informed decisions and giving them the opportunity to improve agricultural productivity. Written by Mehmet Metin Özgüven, an expert who has conducted field studies and with a working technical knowledge of various topics pertaining to the agriculture age, this book covers many subjects important to the age of digital agriculture, including precision agriculture and livestock farming, using agricultural robots and unmanned arial vehicles in agriculture practices, and image processing and machine vision. It is an essential read for researchers, agriculture sector workers, and agricultural engineers.
