Download or read book Essential Math Skills Over 250 Activities to Develop Deep Understanding written by Bob Sornson and published by Shell Education. This book was released on 2017-05-15 with total page 282 pages. Available in PDF, EPUB and Kindle. Book excerpt: Support and assess the learning of essential skills needed for students' mathematics success! Created to support College and Career Readiness and other state standards, this resource is a great tool for educators. This must-have professional book allows teachers to systematically monitor students' progress toward proficiency in every essential skill. The 250 activities provide a rich menu of math learning experiences, which includes the use of manipulatives, activities, exploration, inquiry, and play. Digital resources are also provided and include student activity pages and teacher resources.
Download or read book Essential Math Skills Over 250 Activities to Develop Deep Learning written by Bob Sornson and published by Teacher Created Materials. This book was released on 2014-04-01 with total page 170 pages. Available in PDF, EPUB and Kindle. Book excerpt: The ultimate resource for establishing a solid foundation for mathematical proficiency, Essential Math Skills provides hundreds of engaging, easy-to-implement activities and practical assessment tools. This standards- and research-based resource identifies the core math skills that must be measured at each grade level in Pre-K through third grade. Teachers can easily identify the skills from earlier grades that may need reteaching as well as appropriate activities for students who are ready to tackle higher-level skills. Students build confidence as they develop deep understanding and successfully advance through the skills. The creative strategies presented for teaching each skill include the use of manipulatives, visual-motor activities, exploration, inquiry, and play. When they experience success with these fun tasks, students can't help but fall in love with math!
Download or read book Math for Deep Learning written by Ronald T. Kneusel and published by No Starch Press. This book was released on 2021-11-23 with total page 346 pages. Available in PDF, EPUB and Kindle. Book excerpt: Math for Deep Learning provides the essential math you need to understand deep learning discussions, explore more complex implementations, and better use the deep learning toolkits. With Math for Deep Learning, you'll learn the essential mathematics used by and as a background for deep learning. You’ll work through Python examples to learn key deep learning related topics in probability, statistics, linear algebra, differential calculus, and matrix calculus as well as how to implement data flow in a neural network, backpropagation, and gradient descent. You’ll also use Python to work through the mathematics that underlies those algorithms and even build a fully-functional neural network. In addition you’ll find coverage of gradient descent including variations commonly used by the deep learning community: SGD, Adam, RMSprop, and Adagrad/Adadelta.
Download or read book Inside Deep Learning written by Edward Raff and published by Simon and Schuster. This book was released on 2022-07-05 with total page 598 pages. Available in PDF, EPUB and Kindle. Book excerpt: Journey through the theory and practice of modern deep learning, and apply innovative techniques to solve everyday data problems. In Inside Deep Learning, you will learn how to: Implement deep learning with PyTorch Select the right deep learning components Train and evaluate a deep learning model Fine tune deep learning models to maximize performance Understand deep learning terminology Adapt existing PyTorch code to solve new problems Inside Deep Learning is an accessible guide to implementing deep learning with the PyTorch framework. It demystifies complex deep learning concepts and teaches you to understand the vocabulary of deep learning so you can keep pace in a rapidly evolving field. No detail is skipped—you’ll dive into math, theory, and practical applications. Everything is clearly explained in plain English. About the technology Deep learning doesn’t have to be a black box! Knowing how your models and algorithms actually work gives you greater control over your results. And you don’t have to be a mathematics expert or a senior data scientist to grasp what’s going on inside a deep learning system. This book gives you the practical insight you need to understand and explain your work with confidence. About the book Inside Deep Learning illuminates the inner workings of deep learning algorithms in a way that even machine learning novices can understand. You’ll explore deep learning concepts and tools through plain language explanations, annotated code, and dozens of instantly useful PyTorch examples. Each type of neural network is clearly presented without complex math, and every solution in this book can run using readily available GPU hardware! What's inside Select the right deep learning components Train and evaluate a deep learning model Fine tune deep learning models to maximize performance Understand deep learning terminology About the reader For Python programmers with basic machine learning skills. About the author Edward Raff is a Chief Scientist at Booz Allen Hamilton, and the author of the JSAT machine learning library. Table of Contents PART 1 FOUNDATIONAL METHODS 1 The mechanics of learning 2 Fully connected networks 3 Convolutional neural networks 4 Recurrent neural networks 5 Modern training techniques 6 Common design building blocks PART 2 BUILDING ADVANCED NETWORKS 7 Autoencoding and self-supervision 8 Object detection 9 Generative adversarial networks 10 Attention mechanisms 11 Sequence-to-sequence 12 Network design alternatives to RNNs 13 Transfer learning 14 Advanced building blocks
Download or read book Math for Deep Learning written by Ronald Kneusel and published by . This book was released on 2021 with total page 344 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep learning is everywhere, making this powerful driver of AI something more STEM professionals need to know. Learning which library commands to use is one thing, but to truly understand the discipline, you need to grasp the mathematical concepts that make it tick. This book will give you a working knowledge of topics in probability, statistics, linear algebra, and differential calculus - the essential math needed to make deep learning comprehensible, which is key to practicing it successfully. Each of the four subfields are contextualized with Python code and hands-on, real-world examples that bridge the gap between pure mathematics and its applications in deep learning. Chapters build upon one another, with foundational topics such as Bayes' theorem followed by more advanced concepts, like training neural networks using vectors, matrices, and derivatives of functions. You'll ultimately put all this math to use as you explore and implement deep learning algorithms, including backpropagation and gradient descent - the foundational algorithms that have enabled the AI revolution. You'll learn: •The rules of probability, probability distributions, and Bayesian probability •The use of statistics for understanding datasets and evaluating models •How to manipulate vectors and matrices, and use both to move data through a neural network •How to use linear algebra to implement principal component analysis and singular value decomposition •How to apply improved versions of gradient descent, like RMSprop, Adagrad and Adadelta Once you understand the core math concepts presented throughout this book through the lens of AI programming, you'll have foundational know-how to easily follow and work with deep learning.
Download or read book Probabilistic Graphical Models written by Daphne Koller and published by MIT Press. This book was released on 2009-07-31 with total page 1270 pages. Available in PDF, EPUB and Kindle. Book excerpt: A general framework for constructing and using probabilistic models of complex systems that would enable a computer to use available information for making decisions. Most tasks require a person or an automated system to reason—to reach conclusions based on available information. The framework of probabilistic graphical models, presented in this book, provides a general approach for this task. The approach is model-based, allowing interpretable models to be constructed and then manipulated by reasoning algorithms. These models can also be learned automatically from data, allowing the approach to be used in cases where manually constructing a model is difficult or even impossible. Because uncertainty is an inescapable aspect of most real-world applications, the book focuses on probabilistic models, which make the uncertainty explicit and provide models that are more faithful to reality. Probabilistic Graphical Models discusses a variety of models, spanning Bayesian networks, undirected Markov networks, discrete and continuous models, and extensions to deal with dynamical systems and relational data. For each class of models, the text describes the three fundamental cornerstones: representation, inference, and learning, presenting both basic concepts and advanced techniques. Finally, the book considers the use of the proposed framework for causal reasoning and decision making under uncertainty. The main text in each chapter provides the detailed technical development of the key ideas. Most chapters also include boxes with additional material: skill boxes, which describe techniques; case study boxes, which discuss empirical cases related to the approach described in the text, including applications in computer vision, robotics, natural language understanding, and computational biology; and concept boxes, which present significant concepts drawn from the material in the chapter. Instructors (and readers) can group chapters in various combinations, from core topics to more technically advanced material, to suit their particular needs.
Download or read book Hands On Mathematics for Deep Learning written by Jay Dawani and published by Packt Publishing Ltd. This book was released on 2020-06-12 with total page 347 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive guide to getting well-versed with the mathematical techniques for building modern deep learning architectures Key FeaturesUnderstand linear algebra, calculus, gradient algorithms, and other concepts essential for training deep neural networksLearn the mathematical concepts needed to understand how deep learning models functionUse deep learning for solving problems related to vision, image, text, and sequence applicationsBook Description Most programmers and data scientists struggle with mathematics, having either overlooked or forgotten core mathematical concepts. This book uses Python libraries to help you understand the math required to build deep learning (DL) models. You'll begin by learning about core mathematical and modern computational techniques used to design and implement DL algorithms. This book will cover essential topics, such as linear algebra, eigenvalues and eigenvectors, the singular value decomposition concept, and gradient algorithms, to help you understand how to train deep neural networks. Later chapters focus on important neural networks, such as the linear neural network and multilayer perceptrons, with a primary focus on helping you learn how each model works. As you advance, you will delve into the math used for regularization, multi-layered DL, forward propagation, optimization, and backpropagation techniques to understand what it takes to build full-fledged DL models. Finally, you’ll explore CNN, recurrent neural network (RNN), and GAN models and their application. By the end of this book, you'll have built a strong foundation in neural networks and DL mathematical concepts, which will help you to confidently research and build custom models in DL. What you will learnUnderstand the key mathematical concepts for building neural network modelsDiscover core multivariable calculus conceptsImprove the performance of deep learning models using optimization techniquesCover optimization algorithms, from basic stochastic gradient descent (SGD) to the advanced Adam optimizerUnderstand computational graphs and their importance in DLExplore the backpropagation algorithm to reduce output errorCover DL algorithms such as convolutional neural networks (CNNs), sequence models, and generative adversarial networks (GANs)Who this book is for This book is for data scientists, machine learning developers, aspiring deep learning developers, or anyone who wants to understand the foundation of deep learning by learning the math behind it. Working knowledge of the Python programming language and machine learning basics is required.
Download or read book Mathematics for Machine Learning written by Marc Peter Deisenroth and published by Cambridge University Press. This book was released on 2020-04-23 with total page 392 pages. Available in PDF, EPUB and Kindle. Book excerpt: The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.
Download or read book Teaching Mathematics Meaningfully written by David H. Allsopp and published by Brookes Publishing Company. This book was released on 2007 with total page 316 pages. Available in PDF, EPUB and Kindle. Book excerpt: Making mathematics concepts understandable is a challenge for any teacher--a challenge that's more complex when a classroom includes students with learning difficulties. With this highly practical resource, educators will have just what they need to teach mathematics with confidence: research-based strategies that really work with students who have learning disabilities, ADHD, or mild cognitive disabilities. This urgently needed guidebook helps teachers Understand why students struggle.Teachers will discover how the common learning characteristics of students with learning difficulties create barriers to understanding mathematics. Review the Big Ideas. Are teachers focusing on the right things? A helpful primer on major NCTM-endorsed mathematical concepts and processes helps them be sure. Directly address students' learning barriers. With the lesson plans, practical strategies, photocopiable information-gathering forms, and online strategies in action, teachers will have concrete ways to help students grasp mathematical concepts, improve their proficiency, and generalize knowledge in multiple contexts. Check their own strengths and needs. Educators will reflect critically on their current practices with a thought-provoking questionnaire. With this timely book--filled with invaluable ideas and strategies adaptable for grades K-12--educators will know just what to teach and how to teach it to students with learning difficulties.
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.
Download or read book Preparing Teachers for Deeper Learning written by Linda Darling-Hammond and published by Harvard Education Press. This book was released on 2021-02-08 with total page 477 pages. Available in PDF, EPUB and Kindle. Book excerpt: Preparing Teachers for Deeper Learning answers an urgent call for teachers who educate children from diverse backgrounds to meet the demands of a changing world. In today’s knowledge economy, teachers must prioritize problem-solving ability, adaptability, critical thinking, and the development of interpersonal and collaborative skills over rote memorization and the passive transmission of knowledge. Authors Linda Darling-Hammond and Jeannie Oakes and their colleagues examine what this means for teacher preparation and showcase the work of programs that are educating for deeper learning, equity, and social justice. Guided by the growing knowledge base in the science of learning and development, the book examines teacher preparation programs at Alverno College, Bank Street College of Education, High Tech High’s Intern Program, Montclair State University, San Francisco Teacher Residency, Trinity University, and University of Colorado Denver. These seven programs share a common understanding of how people learn that shape similar innovative practices. With vivid examples of teaching for deeper learning in coursework and classrooms; interviews with faculty, school partners, and novice teachers; surveys of teacher candidates and graduates; and analyses of curriculum and practices, Preparing Teachers for Deeper Learning depicts transformative forms of teaching and teacher preparation that honor and expand all students’ abilities, knowledges, and experiences, and reaffirm the promise of educating for a better world.
Download or read book Assessing Comprehension Thinking Strategies written by Ellin Keene and published by Teacher Created Materials. This book was released on 2006-06-28 with total page 91 pages. Available in PDF, EPUB and Kindle. Book excerpt: Developed by renowned author Ellin Keene, Assessing Comprehension Thinking Strategies is an ideal tool for assessing students' reading comprehension. This book offers a unique way of assessing how students use thinking strategies to comprehend text. The book contains four reading passages for each grade level (1-8) that offer high-interest fiction and nonfiction text. Each assessment is accompanied by a rubric that allows you to document students' thinking and then score and monitor their growth. Strategies assessed include thinking aloud, using schema, inferring, asking questions, determining.
Download or read book In Search of Deeper Learning written by Jal Mehta and published by Harvard University Press. This book was released on 2019-04-22 with total page 465 pages. Available in PDF, EPUB and Kindle. Book excerpt: "The best book on high school dynamics I have ever read."--Jay Mathews, Washington Post An award-winning professor and an accomplished educator take us beyond the hype of reform and inside some of America's most innovative classrooms to show what is working--and what isn't--in our schools. What would it take to transform industrial-era schools into modern organizations capable of supporting deep learning for all? Jal Mehta and Sarah Fine's quest to answer this question took them inside some of America's most innovative schools and classrooms--places where educators are rethinking both what and how students should learn. The story they tell is alternately discouraging and hopeful. Drawing on hundreds of hours of observations and interviews at thirty different schools, Mehta and Fine reveal that deeper learning is more often the exception than the rule. And yet they find pockets of powerful learning at almost every school, often in electives and extracurriculars as well as in a few mold-breaking academic courses. These spaces achieve depth, the authors argue, because they emphasize purpose and choice, cultivate community, and draw on powerful traditions of apprenticeship. These outliers suggest that it is difficult but possible for schools and classrooms to achieve the integrations that support deep learning: rigor with joy, precision with play, mastery with identity and creativity. This boldly humanistic book offers a rich account of what education can be. The first panoramic study of American public high schools since the 1980s, In Search of Deeper Learning lays out a new vision for American education--one that will set the agenda for schools of the future.
Download or read book Singapore Math Challenge Grades 2 5 written by Frank Schaffer Publications and published by Carson-Dellosa Publishing. This book was released on 2013-02-01 with total page 354 pages. Available in PDF, EPUB and Kindle. Book excerpt: Get ready to take the Math Challenge! Singapore Math Challenge will provide second grade students with skill-building practice based on the leading math program in the world, Singapore Math! Common Core Standards accelerate math expectations for all students, creating a need for challenging supplementary math practice. Singapore Math Challenge is the ideal solution, with problems, puzzles, and brainteasers that strengthen mathematical thinking. Step-by-step strategies are clearly explained for solving problems at varied levels of difficulty. A complete, worked solution is also provided for each problem. -- Singapore Math Challenge includes the tools and practice needed to provide a strong mathematical foundation and ongoing success for your students. The Common Core State Standards cite Singapore math standards as worldwide benchmarks for excellence in mathematics.
Download or read book Coding the Matrix written by Philip N. Klein and published by . This book was released on 2013-07 with total page 530 pages. Available in PDF, EPUB and Kindle. Book excerpt: An engaging introduction to vectors and matrices and the algorithms that operate on them, intended for the student who knows how to program. Mathematical concepts and computational problems are motivated by applications in computer science. The reader learns by "doing," writing programs to implement the mathematical concepts and using them to carry out tasks and explore the applications. Examples include: error-correcting codes, transformations in graphics, face detection, encryption and secret-sharing, integer factoring, removing perspective from an image, PageRank (Google's ranking algorithm), and cancer detection from cell features. A companion web site, codingthematrix.com provides data and support code. Most of the assignments can be auto-graded online. Over two hundred illustrations, including a selection of relevant "xkcd" comics. Chapters: "The Function," "The Field," "The Vector," "The Vector Space," "The Matrix," "The Basis," "Dimension," "Gaussian Elimination," "The Inner Product," "Special Bases," "The Singular Value Decomposition," "The Eigenvector," "The Linear Program" A new edition of this text, incorporating corrections and an expanded index, has been issued as of September 4, 2013, and will soon be available on Amazon.
Download or read book Literacy Strategies for Improving Mathematics Instruction written by Joan M. Kenney and published by ASCD. This book was released on 2005 with total page 122 pages. Available in PDF, EPUB and Kindle. Book excerpt: An eyeopening look at how teachers can use literacy strategies to help students better understand mathematics.
Download or read book Dive Into Deep Learning written by Joanne Quinn and published by Corwin Press. This book was released on 2019-07-15 with total page 297 pages. Available in PDF, EPUB and Kindle. Book excerpt: The leading experts in system change and learning, with their school-based partners around the world, have created this essential companion to their runaway best-seller, Deep Learning: Engage the World Change the World. This hands-on guide provides a roadmap for building capacity in teachers, schools, districts, and systems to design deep learning, measure progress, and assess conditions needed to activate and sustain innovation. Dive Into Deep Learning: Tools for Engagement is rich with resources educators need to construct and drive meaningful deep learning experiences in order to develop the kind of mindset and know-how that is crucial to becoming a problem-solving change agent in our global society. Designed in full color, this easy-to-use guide is loaded with tools, tips, protocols, and real-world examples. It includes: • A framework for deep learning that provides a pathway to develop the six global competencies needed to flourish in a complex world — character, citizenship, collaboration, communication, creativity, and critical thinking. • Learning progressions to help educators analyze student work and measure progress. • Learning design rubrics, templates and examples for incorporating the four elements of learning design: learning partnerships, pedagogical practices, learning environments, and leveraging digital. • Conditions rubrics, teacher self-assessment tools, and planning guides to help educators build, mobilize, and sustain deep learning in schools and districts. Learn about, improve, and expand your world of learning. Put the joy back into learning for students and adults alike. Dive into deep learning to create learning experiences that give purpose, unleash student potential, and transform not only learning, but life itself.
