Products related to Deep:
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Practical TensorFlow.js : Deep Learning in Web App Development
Develop and deploy deep learning web apps using the TensorFlow.js library.TensorFlow.?js? is part of a bigger framework named TensorFlow, which has many tools that supplement it, such as TensorBoard?, ?ml5js?, ?tfjs-vis.This book will cover all these technologies and show they integrate with TensorFlow.?js? to create intelligent web apps. The most common and accessible platform users interact with everyday is their web browser, making it an ideal environment to deploy AI systems.TensorFlow.js is a well-known and battle-tested library for creating browser solutions.Working in JavaScript, the so-called language of the web, directly on a browser, you can develop and serve deep learning applications.You'll work with deep learning algorithms such as feedforward neural networks, convolutional neural networks (CNN), recurrent neural networks (RNN), and generative adversarial network (GAN).Through hands-on examples, apply these networks in use cases related to image classification, natural language processing, object detection, dimensionality reduction, image translation, transfer learning, and time series analysis. Also, these topics are very varied in terms of the kind of data they use, their output, and the training phase.Not everything in machine learning is deep networks, there is also what some call shallow or traditional machine learning.While TensorFlow.js is not the most common place to implement these, you'll be introduce them and review the basics of machine learning through TensorFlow.js. What You'll LearnBuild deep learning products suitable for web browsersWork with deep learning algorithms such as feedforward neural networks, convolutional neural networks (CNN), recurrent neural networks (RNN), and generative adversarial network (GAN)Develop apps using image classification, natural language processing, object detection, dimensionality reduction, image translation, transfer learning, and time series analysisWho This Book Is For Programmers developing deep learning solutions for the web and those who want to learn TensorFlow.js with at least minimal programming and software development knowledge.No prior JavaScript knowledge is required, but familiarity with it is helpful.
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Teachers Learning : Professional Development and Education
A collaborative series with the University of Cambridge Faculty of Education highlighting leading-edge research across Teacher Education, International Education Reform and Language Education. Teachers Learning focuses on continuing teacher's professional development through accounts of recent enquiries.From presenting evidence and arguments about the teacher's role and profession, knowledge and knowledge creation, other organisations in the educational landscape, issues of power, status and development, this book highlights the crucial importance and effectiveness of teachers' learning benefitting pupils and classroom learning.
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Deep Learning
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.
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Deep Learning
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 SpaceXDeep 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.
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Why deep learning compared to machine learning?
Deep learning is a subset of machine learning that uses neural networks to learn from data. It is more powerful than traditional machine learning techniques because it can automatically discover and learn from complex patterns and features in the data without the need for explicit feature engineering. Deep learning can handle large amounts of data and is capable of learning from unstructured data such as images, audio, and text, making it more versatile and effective for a wide range of applications. Additionally, deep learning models can continuously improve their performance with more data, making them more adaptable and scalable compared to traditional machine learning models.
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Have I understood Deep Learning correctly?
Deep Learning is a subset of machine learning that uses neural networks to learn from data. It involves training a model on a large amount of data to recognize patterns and make predictions. Deep Learning is used in various applications such as image and speech recognition, natural language processing, and autonomous vehicles. It requires a large amount of computational power and data to train the models effectively.
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What is the definition of deep learning?
Deep learning is a subset of machine learning that uses artificial neural networks to learn from data. It involves training these neural networks with large amounts of labeled data to recognize patterns and make decisions or predictions. Deep learning algorithms are able to automatically learn and improve from experience without being explicitly programmed, making them well-suited for tasks such as image and speech recognition, natural language processing, and other complex data analysis.
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What is the difference between Deep Learning and Machine Learning?
Deep learning is a subset of machine learning that uses artificial neural networks to learn from data. It involves training these neural networks with large amounts of labeled data to make predictions or decisions. Machine learning, on the other hand, is a broader field that encompasses various techniques and algorithms for computers to learn from data and make predictions without being explicitly programmed. While machine learning can involve simpler algorithms like decision trees or support vector machines, deep learning typically involves more complex neural network architectures and requires a large amount of data for training.
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Employing Community-Based Experiential Learning in Teacher Education
This book positions itself at the intersection of the interrelationship between three key areas of initial teacher education: constructivist learning theories, teaching practicum, and the promotion of reflective practices.It presents an innovative approach to teacher preparation at undergraduate and postgraduate levels by critically examining the implementation of a mandatory experiential learning block across subject disciplines on undergraduate and postgraduate teacher preparation courses.This book presents multiple examples and case studies of these varied experiential learning projects that will inform academics, teachers and policymakers.Through these rich examples the authors set out to address the theory-practice dilemma in teacher education, where teachers-to-be are often positioned as ‘consumers’ of educational research in classrooms, read reference books and academic papers on teaching, and observe university and school experts before applying the same acquired theories and practices in their own classes.In the book the authors argue for a shift away from this conventional teacher-learning curriculum that is characterised by the separation of theory and practice, choosing instead to promote pedagogy and methods courses where practice underpins all learning.These pedagogical perspectives include the promotion of a diverse range of learning contexts (including on- and off-campus learning sites) for student teachers to experience during their time on teacher education courses.
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Toward Positive Youth Development : Transforming Schools and Community Programs
Social settings have enormous power to promote or hinder positive youth development.Researchers and practitioners know a great deal about features of schools and programs for youth that affect development, but much less about how to transform settings to bring about these desirable features.This book shows how to harness the power of settings.It shifts the debate from simply enhancing youth outcomes at the individual level to improving the settings of youths' daily lives.The book offers researchers and practitioners blueprints for creating and changing influential settings including classrooms, schools, universities, out-of-school time programs, ethnic systems of supplementary education, and other community-based programs.Leading scholars in psychology, education, human development, sociology, anthropology, economics, law, and public policy discuss a wide array of social change strategies, and describe how to measure key features of settings as a target and guide for change.The authors also demonstrate how larger social structures - such as school districts, community coalitions, community data resources - can support change.Many of the chapters describe ways to make settings work for all youth, including those marginalized by reason of race, ethnicity, social class, or sexual orientation.Toward Positive Youth Development will guide researchers, educators, administrators and policy makers to improve schools and youth programs for all of America's youth.
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Introduction to Community Development : Theory, Practice, and Service-Learning
Historical context, theoretical grounding, critical issues perspective, and the "how-to" for community developmentThis collection of work by leading community development scholars presents students with a theoretical and practical introduction to the field.The text progresses seamlessly from a theoretical overview to a historical overview to three approaches to community development (ecological, interactional, and structural-functional), then explores the practice of community development along with technical assistance, action research, evaluation research, and the role of local organizations, local leadership, and coalitions.The book concludes with critical issues, such as rural development, inner-city development, youth in community development, health care, public schools, and sustainable development. Key features include:New community development intervention models, using examples from recent events to demonstrate the strengths and challenges of such models Community service-learning activities, providing useful examples and case studies of successful service-learning programs in communities A hierarchy of learning activities and exercises within each chapter, ensuring that students learn how to apply concepts to real-world issues Relevant cases of successful community development interventions, giving students examples of 'best practices'Major trends, challenges, and prospects for the future in community development, helping students identify key issues and opportunities Instructor Teaching Site: Password-protected resources contain learning modules, developed by the authors, which include exercises and activities which reinforce chapter content, and demonstrate application of concepts in real-life communities. Student Study Site: Open-access study materials include chapter self-quizzes to aid content learning. This comprehensive, introductory survey text is designed for upper-level undergraduate and graduate courses in Community Development, Urban Studies, Rural Development, and Introduction to Service Learning found in departments of sociology, public administration, management, and social work.
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Deep Reading, Deep Learning : Deep Reading Volume 2
We have developed this volume, Deep Reading, Deep Learning, as a companion to our 2017 NCTE book, Deep Reading: Teaching Reading in the Writing Classroom, which received the CCCC Outstanding Book Award in 2019 for Best Edited Collection.In this volume we address a range of social, ethical, and pedagogical issues that have emerged as essential concerns for teachers of reading and writing, especially those related to identity, culture, and positionality.This new volume emphasizes the broad question of equity and social justice in the acquisition and practice of literacy, and the multifaceted lived reality of positionality related to race, class, gender, disability, and language as experienced by students in the classroom.
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How does face recognition work with deep learning?
Face recognition with deep learning works by using a deep neural network to learn and extract features from facial images. The network is trained on a large dataset of labeled facial images, learning to identify unique facial features and patterns. Once trained, the network can then be used to recognize and classify faces in new images by comparing the extracted features with those in its database. Deep learning allows for more accurate and robust face recognition by automatically learning and adapting to different facial variations and conditions.
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How deep is the Challenger Deep?
The Challenger Deep is the deepest known point in the Earth's oceans, reaching a depth of about 36,070 feet (10,994 meters).
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What are the prerequisites for Deep Learning with Python?
The prerequisites for Deep Learning with Python include a solid understanding of Python programming language, familiarity with basic machine learning concepts, such as neural networks and optimization algorithms, and knowledge of linear algebra and calculus. Additionally, having experience with libraries such as NumPy, Pandas, and Matplotlib can be beneficial for data manipulation and visualization tasks. Finally, a strong foundation in statistics and probability theory is also recommended for understanding the underlying principles of deep learning algorithms.
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Which deep fryer?
When choosing a deep fryer, it is important to consider the size and capacity you need based on the amount of food you typically fry. Additionally, look for features such as adjustable temperature control, a timer, and a viewing window to monitor the cooking process. Consider the ease of cleaning and maintenance, as well as safety features like cool-touch handles and automatic shut-off. Finally, think about whether you prefer a traditional deep fryer with a basket or an air fryer for a healthier cooking option.
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