Guided Math Stretch: Pattern Tables--The In/Out Machine PDF Download
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Author: Lanney Sammons Publisher: Teacher Created Materials ISBN: 1425880320 Category : Languages : en Pages : 6
Book Description
Engage your mathematics students at the beginning of class with this whole-class warm-up activity. This product features a step-by-step lesson, assessment information, and a snapshot of what the warm-up looks like in the classroom.
Author: Lanney Sammons Publisher: Teacher Created Materials ISBN: 1425880320 Category : Languages : en Pages : 6
Book Description
Engage your mathematics students at the beginning of class with this whole-class warm-up activity. This product features a step-by-step lesson, assessment information, and a snapshot of what the warm-up looks like in the classroom.
Author: Marc Peter Deisenroth Publisher: Cambridge University Press ISBN: 1108569323 Category : Computers Languages : en Pages : 392
Book Description
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.
Author: Reagan Tunstall Publisher: Dave Burgess Consulting ISBN: 9781951600761 Category : Languages : en Pages : 124
Book Description
In today's classrooms, the instructional needs and developmental levels of our students are highly varied, and the conventional math whole-group model has its downsides. In contrast to the rigid, one-size-ts-all approach of conventional whole-group instruction, guided math allows us to structure our math block to support student learning in risk-free, small-group instruction. Guided math goes beyond just reorganizing your math block; it also gives you an opportunity to approach math instruction with a renewed sense of perspective and purpose. Drawing on two decades of experience, Reagan Tunstall oers step-by-step best practices to help educators revolutionize their math blocks with a student-centered approach. Whether you're a new teacher who's curious about guided math or a veteran educator looking to hone your methodology, Guided Math AMPED will transform your math block into an exciting and engaging encounter that encourages your students to see themselves as genuine mathematicians. "Most educators have come to realize that the magic happens at the teacher table or during small-group instruction. If that's the case, Guided Math AMPED is the spell book." -JENNIFER SALYARDS, M.Ed., principal, Chamberlin Elementary, Stephenville ISD "Guided Math AMPED provides educators with a practical framework for enhancing math instruction in a way that provides research-based practices, differentiated instruction, and fun, all while strengthening relationships with students and developing math mindsets. No matter your experience or tenure in education, Guided Math AMPED will give you tips and tricks to implement in your classroom." -MATT BERES, district administrator, Wooster, OH "Guided math is one of the best things you can implement in your classroom, and Reagan Tunstall is the best to learn from, thanks to her perfect framework and step-by-step instructions. She has thought through every potential roadblock and offers concise solutions because she's experienced it all in her own classroom." -HALEE SIKORSKI, educator, A Latte Learning "Don't you dare let another teacher borrow this book . . . you may never get it back! From the rst page to the end, this book is lled with practical ideas and guidelines guaranteed to take your guided math block to the next level." -LORI MCDONALD, M.Ed., retired educator
Author: Charles C Pinter Publisher: Courier Corporation ISBN: 0486474178 Category : Mathematics Languages : en Pages : 402
Book Description
Accessible but rigorous, this outstanding text encompasses all of the topics covered by a typical course in elementary abstract algebra. Its easy-to-read treatment offers an intuitive approach, featuring informal discussions followed by thematically arranged exercises. This second edition features additional exercises to improve student familiarity with applications. 1990 edition.
Author: Paul Orland Publisher: Manning Publications ISBN: 1617295353 Category : Computers Languages : en Pages : 686
Book Description
In Math for Programmers you’ll explore important mathematical concepts through hands-on coding. Filled with graphics and more than 300 exercises and mini-projects, this book unlocks the door to interesting–and lucrative!–careers in some of today’s hottest fields. As you tackle the basics of linear algebra, calculus, and machine learning, you’ll master the key Python libraries used to turn them into real-world software applications. Summary To score a job in data science, machine learning, computer graphics, and cryptography, you need to bring strong math skills to the party. Math for Programmers teaches the math you need for these hot careers, concentrating on what you need to know as a developer. Filled with lots of helpful graphics and more than 200 exercises and mini-projects, this book unlocks the door to interesting–and lucrative!–careers in some of today’s hottest programming fields. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Skip the mathematical jargon: This one-of-a-kind book uses Python to teach the math you need to build games, simulations, 3D graphics, and machine learning algorithms. Discover how algebra and calculus come alive when you see them in code! About the book In Math for Programmers you’ll explore important mathematical concepts through hands-on coding. Filled with graphics and more than 300 exercises and mini-projects, this book unlocks the door to interesting–and lucrative!–careers in some of today’s hottest fields. As you tackle the basics of linear algebra, calculus, and machine learning, you’ll master the key Python libraries used to turn them into real-world software applications. What's inside Vector geometry for computer graphics Matrices and linear transformations Core concepts from calculus Simulation and optimization Image and audio processing Machine learning algorithms for regression and classification About the reader For programmers with basic skills in algebra. About the author Paul Orland is a programmer, software entrepreneur, and math enthusiast. He is co-founder of Tachyus, a start-up building predictive analytics software for the energy industry. You can find him online at www.paulor.land. Table of Contents 1 Learning math with code PART I - VECTORS AND GRAPHICS 2 Drawing with 2D vectors 3 Ascending to the 3D world 4 Transforming vectors and graphics 5 Computing transformations with matrices 6 Generalizing to higher dimensions 7 Solving systems of linear equations PART 2 - CALCULUS AND PHYSICAL SIMULATION 8 Understanding rates of change 9 Simulating moving objects 10 Working with symbolic expressions 11 Simulating force fields 12 Optimizing a physical system 13 Analyzing sound waves with a Fourier series PART 3 - MACHINE LEARNING APPLICATIONS 14 Fitting functions to data 15 Classifying data with logistic regression 16 Training neural networks
Author: National Research Council Publisher: National Academies Press ISBN: 0309131987 Category : Education Languages : en Pages : 53
Book Description
Results from national and international assessments indicate that school children in the United States are not learning mathematics well enough. Many students cannot correctly apply computational algorithms to solve problems. Their understanding and use of decimals and fractions are especially weak. Indeed, helping all children succeed in mathematics is an imperative national goal. However, for our youth to succeed, we need to change how we're teaching this discipline. Helping Children Learn Mathematics provides comprehensive and reliable information that will guide efforts to improve school mathematics from pre-kindergarten through eighth grade. The authors explain the five strands of mathematical proficiency and discuss the major changes that need to be made in mathematics instruction, instructional materials, assessments, teacher education, and the broader educational system and answers some of the frequently asked questions when it comes to mathematics instruction. The book concludes by providing recommended actions for parents and caregivers, teachers, administrators, and policy makers, stressing the importance that everyone work together to ensure a mathematically literate society.
Author: Ian Goodfellow Publisher: MIT Press ISBN: 0262337371 Category : Computers Languages : en Pages : 801
Book Description
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.
Author: National Research Council Publisher: National Academies Press ISBN: 0309147433 Category : Education Languages : en Pages : 399
Book Description
Early childhood mathematics is vitally important for young children's present and future educational success. Research demonstrates that virtually all young children have the capability to learn and become competent in mathematics. Furthermore, young children enjoy their early informal experiences with mathematics. Unfortunately, many children's potential in mathematics is not fully realized, especially those children who are economically disadvantaged. This is due, in part, to a lack of opportunities to learn mathematics in early childhood settings or through everyday experiences in the home and in their communities. Improvements in early childhood mathematics education can provide young children with the foundation for school success. Relying on a comprehensive review of the research, Mathematics Learning in Early Childhood lays out the critical areas that should be the focus of young children's early mathematics education, explores the extent to which they are currently being incorporated in early childhood settings, and identifies the changes needed to improve the quality of mathematics experiences for young children. This book serves as a call to action to improve the state of early childhood mathematics. It will be especially useful for policy makers and practitioners-those who work directly with children and their families in shaping the policies that affect the education of young children.
Author: Lanney Sammons
Author: Lanney Sammons
Author: McGraw Hill
Author: Marc Peter Deisenroth
Author: Reagan Tunstall
Author: Charles C Pinter
Author: Paul Orland
Author: National Research Council
Author: Ray Appel
Author: Ian Goodfellow
Author: National Research Council