Products related to Deep:
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Teaching, Tutoring and Training in the Lifelong Learning Sector
This core text provides comprehensive support for pre-service and in-service trainee teachers in the Lifelong Learning Sector covering all they need to know to achieve QTLS status. Supporting trainees through all stages of their professional development, the text takes the reader through the theoretical background underpinning teaching and learning and offers practical guidance on day-to-day challenges. This fourth edition has been fully revised and updated and includes a new chapter on teaching practice with notes on observation and lesson planning.New information on behaviour management has been added to support trainees in an aspect of teaching that many find challenging.
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Computer Science Education : Perspectives on Teaching and Learning in School
Drawing together the most up-to-date research from experts all across the world, the second edition of Computer Science Education offers the most up-to-date coverage available on this developing subject, ideal for building confidence of new pre-service and in-service educators teaching a new discipline.It provides an international overview of key concepts, pedagogical approaches and assessment practices. Highlights of the second edition include:- New sections on machine learning and data-driven (epistemic) programming- A new focus on equity and inclusion in computer science education- Chapters updated throughout, including a revised chapter on relating ethical and societal aspects to knowledge-rich aspects of computer science education- A new set of chapters on the learning of programming, including design, pedagogy and misconceptions- A chapter on the way we use language in the computer science classroom. The book is structured to support the reader with chapter outlines, synopses and key points.Explanations of key concepts, real-life examples and reflective points keep the theory grounded in classroom practice. The book is accompanied by a companion website, including online summaries for each chapter, 3-minute video summaries by each author and an archived chapter on taxonomies and competencies from the first edition.
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Inspiring Deep Learning with Metacognition : A Guide for Secondary Teaching
Understand what metacognition is and how you can apply it to your secondary school teaching to support deep and effective learning in your classroom. Metacognition is a popular topic in teaching and learning debates, but it’s rarely clearly defined and can be difficult for teachers to understand how it can be applied in the classroom.This book offers a clear introduction to applying metacognition in secondary teaching, exploring the ‘what’, ‘when/how’ and ‘why’ of using metacognition in classrooms with real life examples of how this works in practice. This is a detailed and accessible resource that offers guidance that teachers can start applying to their own lesson planning immediately, across secondary subjects. Nathan Burns is the founder of @MetacognitionU and has written metacognitive teaching resources for TES and Oxford University Press.He is Head of Maths in a Derbyshire school.
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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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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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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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Reflective Teaching and Learning in Further Education
This book looks at critical reflection as a key skill for all teachers in further education (FE) and an important part of the new Professional Standards.In particular the text explores the key themes of self-awareness, planning, managing behaviour and CPD in relation to reflective practice to demonstrate how it can support those areas of teaching that most often cause concern.The limitations and benefits of reflection are analysed and action research is identified as an important facet in developing professional reflective practice which can in turn enhance both the personal and professional life of FE teachers. Â
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Understanding Teaching and Learning in Primary Education
This textbook gives you guidance and insights into the knowledge, values and commitments necessary to succeed in the primary classroom, supported by links to theory and research literature and realistic scenarios you may encounter as a new teacher. Fully updated throughout, key features of this second edition include:· A new chapter on inclusive education· Newly expanded coverage of digital learning, engaging with educational research and the role of the primary teacher· New ‘View from Practice’ examples· Cross-referenced links to the Teachers’ Standards in England and the GTCS Professional Standards in Scotland and where they are covered within the bookThis is essential reading for professional studies modules on primary initial teacher education courses, including university-based (PGCE, PGDE, BA QTS, BEd), school-based (SCITT, School Direct) and employment-based routes into teaching.
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Education, Teaching, and Learning : Discourses, Cultures, Conversations
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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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