Download or read book Mathematics Inside the Black Box written by Dylan Wiliam and published by Granada Learning. This book was released on 2006 with total page 28 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Inside the black box written by Paul Black and published by Granada Learning. This book was released on 1998 with total page 24 pages. Available in PDF, EPUB and Kindle. Book excerpt: Offers practical advice on using and improving assessment for learning in the classroom.
Download or read book Working Inside the Black Box written by Paul Black and published by Granada Learning. This book was released on 2004 with total page 28 pages. Available in PDF, EPUB and Kindle. Book excerpt: Offers practical advice on using and improving assessment for learning in the classroom.
Download or read book The Power of Assessment for Learning written by Margaret Heritage and published by Corwin. This book was released on 2019-11-15 with total page 145 pages. Available in PDF, EPUB and Kindle. Book excerpt: Enrich, grow, and sustain AfL in your classroom. Twenty years after the publication of Inside the Black Box, the landmark review of formative classroom assessment, international education experts Christine Harrison and Margaret Heritage tackle assessment for learning (AfL) anew, with fresh insights gained from two decades of research, theory, and classroom practice. Packed with key AfL ideas and supports, vignettes that illustrate AfL in action, and practice-based evidence to enrich understanding of AfL from both the teacher’s and the student’s perspectives, this book is a ‘sounding board’ for educators to explore and reflect on their own AfL practices and beliefs.
Download or read book Inside the FFT Black Box written by Eleanor Chu and published by CRC Press. This book was released on 1999-11-11 with total page 346 pages. Available in PDF, EPUB and Kindle. Book excerpt: Are some areas of fast Fourier transforms still unclear to you? Do the notation and vocabulary seem inconsistent? Does your knowledge of their algorithmic aspects feel incomplete? The fast Fourier transform represents one of the most important advancements in scientific and engineering computing. Until now, however, treatments have been either brief, cryptic, intimidating, or not published in the open literature. Inside the FFT Black Box brings the numerous and varied ideas together in a common notational framework, clarifying vague FFT concepts. Examples and diagrams explain algorithms completely, with consistent notation. This approach connects the algorithms explicitly to the underlying mathematics. Reviews and explanations of FFT ideas taken from engineering, mathematics, and computer science journals teach the computational techniques relevant to FFT. Two appendices familiarize readers with the design and analysis of computer algorithms, as well. This volume employs a unified and systematic approach to FFT. It closes the gap between brief textbook introductions and intimidating treatments in the FFT literature. Inside the FFT Black Box provides an up-to-date, self-contained guide for learning the FFT and the multitude of ideas and computing techniques it employs.
Download or read book English Inside the Black Box written by Bethan Marshall and published by Granada Learning. This book was released on 2006 with total page 28 pages. Available in PDF, EPUB and Kindle. Book excerpt: English Inside the Black Box is an easy-to-follow booklet offering great advice and guidance on how to develop formative assessment in English.
Download or read book The Brilliance of Black Children in Mathematics written by Jacqueline Leonard and published by IAP. This book was released on 2013-03-01 with total page 399 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a critically important contribution to the work underway to transform schooling for students who have historically been denied access to a quality education, specifically African American children. The first section of the book provides some historical perspective critical to understanding the current state of education in the U.S., specifically for the education of African American children. The following sections include chapters on policy, learning, ethnomathematics, student identity, and teacher preparation as it relates to the mathematical education of Black children. Through offering “counternarratives” about mathematically successful Black youth, advocating for a curriculum that is grounded in African American culture and ways of thinking, providing shining examples of the brilliance of Blacks students, and promoting high expectations for all rather than situating students as the problem, the authors of this book provide powerful insights related to the teaching and learning of mathematics for African American students. As is made evident in this book, effective teaching involves much more than just engaging students in inquiry-based pedagogy (Kitchen, 2003). The chapters offered in this book demonstrate how mathematics instruction for African American students needs to take into account historical marginalization and present-day policies that do harm to Black students (Kunjufu, 2005). Empowering mathematics instruction for African American students needs to take into consideration and promote students’ cultural, spiritual, and historical identities. Furthermore, mathematics instruction for African American students should create opportunities for students to express themselves and the needs of their communities as a means to promote social justice both within their classrooms and communities.
Download or read book Derivative Free and Blackbox Optimization written by Charles Audet and published by Springer. This book was released on 2017-12-02 with total page 302 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is designed as a textbook, suitable for self-learning or for teaching an upper-year university course on derivative-free and blackbox optimization. The book is split into 5 parts and is designed to be modular; any individual part depends only on the material in Part I. Part I of the book discusses what is meant by Derivative-Free and Blackbox Optimization, provides background material, and early basics while Part II focuses on heuristic methods (Genetic Algorithms and Nelder-Mead). Part III presents direct search methods (Generalized Pattern Search and Mesh Adaptive Direct Search) and Part IV focuses on model-based methods (Simplex Gradient and Trust Region). Part V discusses dealing with constraints, using surrogates, and bi-objective optimization. End of chapter exercises are included throughout as well as 15 end of chapter projects and over 40 figures. Benchmarking techniques are also presented in the appendix.
Download or read book Modern Foreign Languages Inside the Black Box written by Jane Jones and published by Granada Learning. This book was released on 2008-01-07 with total page 32 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Assessment For Learning written by Black, Paul and published by McGraw-Hill Education (UK). This book was released on 2003-09-01 with total page 147 pages. Available in PDF, EPUB and Kindle. Book excerpt: Assessment for Learning is based on a two-year project involving thirty-six teachers in schools in Medway and Oxfordshire. After a brief review of the research background and of the project itself, successive chapters describe the specific practices which teachers found fruitful and the underlying ideas about learning that these developments illustrate. Later chapters discuss the problems that teachers encountered when implementing the new practices in their classroom and give guidance for school management and LEAs about promoting and supporting the changes. --from publisher description
Download or read book Science Inside the Black Box written by Paul Joseph Black and published by Granada Learning. This book was released on 2004 with total page 28 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Black Box Optimization Machine Learning and No Free Lunch Theorems written by Panos M. Pardalos and published by Springer Nature. This book was released on 2021-05-27 with total page 388 pages. Available in PDF, EPUB and Kindle. Book excerpt: This edited volume illustrates the connections between machine learning techniques, black box optimization, and no-free lunch theorems. Each of the thirteen contributions focuses on the commonality and interdisciplinary concepts as well as the fundamentals needed to fully comprehend the impact of individual applications and problems. Current theoretical, algorithmic, and practical methods used are provided to stimulate a new effort towards innovative and efficient solutions. The book is intended for beginners who wish to achieve a broad overview of optimization methods and also for more experienced researchers as well as researchers in mathematics, optimization, operations research, quantitative logistics, data analysis, and statistics, who will benefit from access to a quick reference to key topics and methods. The coverage ranges from mathematically rigorous methods to heuristic and evolutionary approaches in an attempt to equip the reader with different viewpoints of the same problem.
Download or read book Math and Architectures of Deep Learning written by Krishnendu Chaudhury and published by Simon and Schuster. This book was released on 2024-05-21 with total page 550 pages. Available in PDF, EPUB and Kindle. Book excerpt: Shine a spotlight into the deep learning “black box”. This comprehensive and detailed guide reveals the mathematical and architectural concepts behind deep learning models, so you can customize, maintain, and explain them more effectively. Inside Math and Architectures of Deep Learning you will find: Math, theory, and programming principles side by side Linear algebra, vector calculus and multivariate statistics for deep learning The structure of neural networks Implementing deep learning architectures with Python and PyTorch Troubleshooting underperforming models Working code samples in downloadable Jupyter notebooks The mathematical paradigms behind deep learning models typically begin as hard-to-read academic papers that leave engineers in the dark about how those models actually function. Math and Architectures of Deep Learning bridges the gap between theory and practice, laying out the math of deep learning side by side with practical implementations in Python and PyTorch. Written by deep learning expert Krishnendu Chaudhury, you’ll peer inside the “black box” to understand how your code is working, and learn to comprehend cutting-edge research you can turn into practical applications. Foreword by Prith Banerjee. About the technology Discover what’s going on inside the black box! To work with deep learning you’ll have to choose the right model, train it, preprocess your data, evaluate performance and accuracy, and deal with uncertainty and variability in the outputs of a deployed solution. This book takes you systematically through the core mathematical concepts you’ll need as a working data scientist: vector calculus, linear algebra, and Bayesian inference, all from a deep learning perspective. About the book Math and Architectures of Deep Learning teaches the math, theory, and programming principles of deep learning models laid out side by side, and then puts them into practice with well-annotated Python code. You’ll progress from algebra, calculus, and statistics all the way to state-of-the-art DL architectures taken from the latest research. What's inside The core design principles of neural networks Implementing deep learning with Python and PyTorch Regularizing and optimizing underperforming models About the reader Readers need to know Python and the basics of algebra and calculus. About the author Krishnendu Chaudhury is co-founder and CTO of the AI startup Drishti Technologies. He previously spent a decade each at Google and Adobe. Table of Contents 1 An overview of machine learning and deep learning 2 Vectors, matrices, and tensors in machine learning 3 Classifiers and vector calculus 4 Linear algebraic tools in machine learning 5 Probability distributions in machine learning 6 Bayesian tools for machine learning 7 Function approximation: How neural networks model the world 8 Training neural networks: Forward propagation and backpropagation 9 Loss, optimization, and regularization 10 Convolutions in neural networks 11 Neural networks for image classification and object detection 12 Manifolds, homeomorphism, and neural networks 13 Fully Bayes model parameter estimation 14 Latent space and generative modeling, autoencoders, and variational autoencoders A Appendix
Download or read book How Not to Be Wrong written by Jordan Ellenberg and published by Penguin. This book was released on 2015-05-26 with total page 482 pages. Available in PDF, EPUB and Kindle. Book excerpt: “Witty, compelling, and just plain fun to read . . ." —Evelyn Lamb, Scientific American The Freakonomics of math—a math-world superstar unveils the hidden beauty and logic of the world and puts its power in our hands The math we learn in school can seem like a dull set of rules, laid down by the ancients and not to be questioned. In How Not to Be Wrong, Jordan Ellenberg shows us how terribly limiting this view is: Math isn’t confined to abstract incidents that never occur in real life, but rather touches everything we do—the whole world is shot through with it. Math allows us to see the hidden structures underneath the messy and chaotic surface of our world. It’s a science of not being wrong, hammered out by centuries of hard work and argument. Armed with the tools of mathematics, we can see through to the true meaning of information we take for granted: How early should you get to the airport? What does “public opinion” really represent? Why do tall parents have shorter children? Who really won Florida in 2000? And how likely are you, really, to develop cancer? How Not to Be Wrong presents the surprising revelations behind all of these questions and many more, using the mathematician’s method of analyzing life and exposing the hard-won insights of the academic community to the layman—minus the jargon. Ellenberg chases mathematical threads through a vast range of time and space, from the everyday to the cosmic, encountering, among other things, baseball, Reaganomics, daring lottery schemes, Voltaire, the replicability crisis in psychology, Italian Renaissance painting, artificial languages, the development of non-Euclidean geometry, the coming obesity apocalypse, Antonin Scalia’s views on crime and punishment, the psychology of slime molds, what Facebook can and can’t figure out about you, and the existence of God. Ellenberg pulls from history as well as from the latest theoretical developments to provide those not trained in math with the knowledge they need. Math, as Ellenberg says, is “an atomic-powered prosthesis that you attach to your common sense, vastly multiplying its reach and strength.” With the tools of mathematics in hand, you can understand the world in a deeper, more meaningful way. How Not to Be Wrong will show you how.
Download or read book Embedded Formative Assessment written by Dylan Wiliam and published by New Art and Science of Teachin. This book was released on 2017-10-20 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Acknowledgments"--"Table of Contents"--"About the Author" -- "Introduction" -- "Chapter 1" -- "Chapter 2" -- "Chapter 3" -- "Chapter 4" -- "Chapter 5" -- "Chapter 6" -- "Chapter 7" -- "Epilogue" -- "Appendix" -- "References and Resources
Download or read book Building Thinking Classrooms in Mathematics Grades K 12 written by Peter Liljedahl and published by Corwin Press. This book was released on 2020-09-28 with total page 454 pages. Available in PDF, EPUB and Kindle. Book excerpt: A thinking student is an engaged student Teachers often find it difficult to implement lessons that help students go beyond rote memorization and repetitive calculations. In fact, institutional norms and habits that permeate all classrooms can actually be enabling "non-thinking" student behavior. Sparked by observing teachers struggle to implement rich mathematics tasks to engage students in deep thinking, Peter Liljedahl has translated his 15 years of research into this practical guide on how to move toward a thinking classroom. Building Thinking Classrooms in Mathematics, Grades K–12 helps teachers implement 14 optimal practices for thinking that create an ideal setting for deep mathematics learning to occur. This guide Provides the what, why, and how of each practice and answers teachers’ most frequently asked questions Includes firsthand accounts of how these practices foster thinking through teacher and student interviews and student work samples Offers a plethora of macro moves, micro moves, and rich tasks to get started Organizes the 14 practices into four toolkits that can be implemented in order and built on throughout the year When combined, these unique research-based practices create the optimal conditions for learner-centered, student-owned deep mathematical thinking and learning, and have the power to transform mathematics classrooms like never before.
Download or read book Geography Inside the Black Box written by Paul Weeden and published by Granada Learning. This book was released on 2006 with total page 28 pages. Available in PDF, EPUB and Kindle. Book excerpt:
