Products related to Linear:
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Non-Linear Perspectives on Teacher Development : Complexity in Professional Learning and Practice
Despite the multifaceted complexity of teaching, dominant perspectives conceptualize teacher development in linear, dualistic, transactional, human-centric ways.The authors in this book offer non-linear alternatives by drawing on a continuum of complex perspectives, including CHAT, complexity theory, actor network theory, indigenous studies, rhizomatics, and posthuman/neomaterialisms.The chapters included here illuminate how different ways of thinking can help us better examine how teachers learn (relationally, with human, material, and discursive elements) and offer ways to understand the entangled nature of the relationship between that learning and what emerges in classroom instructional practice.They also present situated illustrations of what those entanglements or assemblages look like in the preservice, induction, and inservice phases, from early childhood to secondary settings, and across multiple continents.Authors provide evidence that research on teacher development should focus on process as much (if not more than) product and show that complexity perspectives can support forward-thinking, assets-based pedagogies.Methodologically, the chapters encourage conceptual creativity and expansion, and support an argument for blurring theory-method and normalising methodological hybridity.Ultimately, this book provides conceptual, theoretical, and methodological tools to understand current educational conditions in late capitalism and imagine otherwise.It was originally published as a special issue of the journal Professional Development in Education.
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Linear Algebra and Learning from Data
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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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Linear Algebra With Machine Learning and Data
This book takes a deep dive into several key linear algebra subjects as they apply to data analytics and data mining.The book offers a case study approach where each case will be grounded in a real-world application.This text is meant to be used for a second course in applications of Linear Algebra to Data Analytics, with a supplemental chapter on Decision Trees and their applications in regression analysis.The text can be considered in two different but overlapping general data analytics categories: clustering and interpolation.Knowledge of mathematical techniques related to data analytics and exposure to interpretation of results within a data analytics context are particularly valuable for students studying undergraduate mathematics.Each chapter of this text takes the reader through several relevant case studies using real-world data.All data sets, as well as Python and R syntax, are provided to the reader through links to Github documentation.Following each chapter is a short exercise set in which students are encouraged to use technology to apply their expanding knowledge of linear algebra as it is applied to data analytics.A basic knowledge of the concepts in a first Linear Algebra course is assumed; however, an overview of key concepts is presented in the Introduction and as needed throughout the text.
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What is linear growth?
Linear growth is a type of growth that occurs at a constant rate over time. It is characterized by a straight line on a graph, where the increase in quantity is consistent over equal intervals. In linear growth, the amount added or gained remains the same for each unit of time. This type of growth is often seen in simple arithmetic progressions or when there is a constant rate of change.
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What is exponential linear growth?
Exponential linear growth refers to a pattern of growth where a quantity increases at a constant rate over a period of time. This type of growth is characterized by a steady increase in the value of the quantity, with the rate of growth proportional to the current value. In exponential linear growth, the quantity being measured grows by a fixed percentage over regular intervals, leading to a continuous and rapid increase in value. This type of growth is often seen in natural phenomena, such as population growth or the spread of diseases.
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What is a task for linear growth?
A task for linear growth is one that involves a constant rate of change over time. This means that the task progresses in a straight line, with the same amount of growth occurring at regular intervals. For example, if a company's sales are increasing by a fixed amount each month, that would be a task for linear growth. Another example could be the steady increase in the height of a plant as it grows over time. In both cases, the growth is consistent and can be represented by a linear equation.
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What growth, linear or exponential, equals 5?
The growth that equals 5 is linear growth. Linear growth is constant and increases by the same amount over each time period. In this case, the growth is steady and consistent, resulting in a linear progression to reach the value of 5. Exponential growth, on the other hand, increases at an accelerating rate and would result in a much higher value than 5.
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Linear Algebra And Optimization With Applications To Machine Learning - Volume I: Linear Algebra For Computer Vision, Robotics, And Machine Learning
This book provides the mathematical fundamentals of linear algebra to practicers in computer vision, machine learning, robotics, applied mathematics, and electrical engineering.By only assuming a knowledge of calculus, the authors develop, in a rigorous yet down to earth manner, the mathematical theory behind concepts such as: vectors spaces, bases, linear maps, duality, Hermitian spaces, the spectral theorems, SVD, and the primary decomposition theorem.At all times, pertinent real-world applications are provided.This book includes the mathematical explanations for the tools used which we believe that is adequate for computer scientists, engineers and mathematicians who really want to do serious research and make significant contributions in their respective fields.
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Learning Curve : Lessons on Leadership, Education, and Personal Growth
Personal development is not easy. So why do so many leadership and self-help books read like an author's highlight reel?Learning Curve pushes back against conventional literature by discussing the real, behind-the-scenes challenges of a developing school leader. Blending authentic anecdotes with relevant research, Learning Curve transforms complicated life principles into useful pearls of wisdom.Whether you are looking for a few helpful ideas-or are searching for a complete mental makeover-Learning Curve serves as a practical resource for motivated, everyday school leaders.
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Linear Algebra for Data Science, Machine Learning, and Signal Processing
Maximise student engagement and understanding of matrix methods in data-driven applications with this modern teaching package.Students are introduced to matrices in two preliminary chapters, before progressing to advanced topics such as the nuclear norm, proximal operators and convex optimization.Highlighted applications include low-rank approximation, matrix completion, subspace learning, logistic regression for binary classification, robust PCA, dimensionality reduction and Procrustes problems.Extensively classroom-tested, the book includes over 200 multiple-choice questions suitable for in-class interactive learning or quizzes, as well as homework exercises (with solutions available for instructors).It encourages active learning with engaging 'explore' questions, with answers at the back of each chapter, and Julia code examples to demonstrate how the mathematics is actually used in practice.A suite of computational notebooks offers a hands-on learning experience for students.This is a perfect textbook for upper-level undergraduates and first-year graduate students who have taken a prior course in linear algebra basics.
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Foundations of Linear and Generalized Linear Models
A valuable overview of the most important ideas and results in statistical modeling Written by a highly-experienced author, Foundations of Linear and Generalized Linear Models is a clear and comprehensive guide to the key concepts and results of linearstatistical models.The book presents a broad, in-depth overview of the most commonly usedstatistical models by discussing the theory underlying the models, R software applications,and examples with crafted models to elucidate key ideas and promote practical modelbuilding. The book begins by illustrating the fundamentals of linear models, such as how the model-fitting projects the data onto a model vector subspace and how orthogonal decompositions of the data yield information about the effects of explanatory variables.Subsequently, the book covers the most popular generalized linear models, which include binomial and multinomial logistic regression for categorical data, and Poisson and negative binomial loglinear models for count data.Focusing on the theoretical underpinnings of these models, Foundations ofLinear and Generalized Linear Models also features: An introduction to quasi-likelihood methods that require weaker distributional assumptions, such as generalized estimating equation methodsAn overview of linear mixed models and generalized linear mixed models with random effects for clustered correlated data, Bayesian modeling, and extensions to handle problematic cases such as high dimensional problemsNumerous examples that use R software for all text data analysesMore than 400 exercises for readers to practice and extend the theory, methods, and data analysisA supplementary website with datasets for the examples and exercises An invaluable textbook for upper-undergraduate and graduate-level students in statistics and biostatistics courses, Foundations of Linear and Generalized Linear Models is also an excellent reference for practicing statisticians and biostatisticians, as well as anyone who is interested in learning about the most important statistical models for analyzing data.
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What is the difference between linear growth and superlinear growth?
Linear growth refers to a steady increase in a quantity over time, where the rate of growth remains constant. Superlinear growth, on the other hand, describes a situation where the rate of growth accelerates over time, resulting in exponential or faster-than-exponential growth. In superlinear growth, the quantity being measured increases at an increasing rate, leading to a more rapid accumulation compared to linear growth.
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What is the difference between linear growth and proportional growth?
Linear growth occurs when a quantity increases by the same amount over a given time period, resulting in a straight line on a graph. Proportional growth, on the other hand, occurs when a quantity increases by a constant proportion or percentage over time, resulting in a curved line on a graph. In linear growth, the increase is constant, while in proportional growth, the rate of increase changes as the quantity grows.
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What is the difference between exponential growth and linear growth?
Exponential growth occurs when a quantity increases at a constant percentage rate over time, resulting in a rapid increase in the value of the quantity. In contrast, linear growth occurs when a quantity increases at a constant rate over time, resulting in a steady and consistent increase in the value of the quantity. Exponential growth leads to a rapid and accelerating increase, while linear growth leads to a steady and predictable increase.
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What is the difference between linear growth and exponential growth?
Linear growth occurs when a quantity increases by a constant amount over a constant time period. This results in a straight line on a graph. Exponential growth, on the other hand, occurs when a quantity increases by a constant percentage over a constant time period. This results in a curve that becomes increasingly steeper over time on a graph. In other words, in linear growth, the rate of increase is constant, while in exponential growth, the rate of increase is constantly increasing.
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