The Mathematics Program Improvement Review
Title | The Mathematics Program Improvement Review PDF eBook |
Author | Ron Pelfrey |
Publisher | ASCD |
Pages | 229 |
Release | 2006 |
Genre | Education |
ISBN | 1416602690 |
How good is your school's mathematics program? Test scores can provide some general trend information, but what you--and your students' parents--really need are specifics about the quality of the curriculum, the effectiveness of the instruction, and the school's overall capacity to support mathematics learning.The Mathematics Program Improvement Review (MPIR) is a proven evaluation process focused on standards for high-quality mathematics programs in grades K-12. Based on research into effective program-evaluation methods, the MPIR approach uses multiple data sources to clarify exactly what is working within an individual school's math program and what is not.Author and MPIR developer Ron Pelfrey has used this process to evaluate mathematics programs in more than 300 rural, urban, and suburban schools and has trained hundreds of educators to conduct reviews. Now this handbook makes the MPIR process and its benefits available to everyone. Inside, you'll find guidelines for training review team members and all the materials needed to conduct a review, including* Lists of standards and indicators for the 10 essential components of an effective mathematics program.* Templates for questionnaires, interviews, and classroom observations.* Detailed evaluation rubrics.* Forms for compiling ratings and generating a final report.Whether used as a basis for informal faculty or departmental discussion, to promote best practices in a particular area (such as curriculum or instruction), or to guide a formal program evaluation, this book will help any school or district apply MPIR tools and procedures to bring about positive change in students' mathematics learning.
Principles of Mathematics Book 2 (Teacher Guide)
Title | Principles of Mathematics Book 2 (Teacher Guide) PDF eBook |
Author | Katherine (Loop) Hannon |
Publisher | Master Books |
Pages | 0 |
Release | 2016-03-22 |
Genre | Juvenile Nonfiction |
ISBN | 9780890519073 |
Teacher Guide for use with Principles of Mathematics Book 2. Katherine Loop's Principles of Mathematics Book 2 guides students through the core principles of algebra-equipping your student for High School success! Teacher Guide includes daily schedule, student worksheets, quizzes, tests, and answer key.
Principles of Mathematics Book 1 Set
Title | Principles of Mathematics Book 1 Set PDF eBook |
Author | Katherine Loop |
Publisher | Master Books |
Pages | 0 |
Release | 2016-09-02 |
Genre | |
ISBN | 9780890519141 |
Katherine Loop has done the remarkable! She has written a solid math course with a truly Biblical worldview. This course goes way beyond the same old Christian math course that teaches math with a few Scriptures sprinkled in and maybe some church-based word problems. This course truly transforms the way we see math. Katherine makes the argument that math is not a neutral subject as most have come to believe. She carefully lays the foundation of how math points to our Creator, the God of the Bible. The nature of God, His Creation, and even the Gospel itself is seen through the study of math. Katherine does a marvelous job of revealing His Glory in this one-of-a-kind math course. Katherine Loop's Principles of Mathematics Biblical Worldview Curriculum is a first of its kind. It takes math to a whole new level students and parents are going to love. It is a guaranteed faith grower!
A Review of the Mathematics Program
Title | A Review of the Mathematics Program PDF eBook |
Author | Sally Erling |
Publisher | |
Pages | 75 |
Release | 1990 |
Genre | Educational evaluation |
ISBN |
Basic Training in Mathematics
Title | Basic Training in Mathematics PDF eBook |
Author | R. Shankar |
Publisher | Springer |
Pages | 371 |
Release | 2013-12-20 |
Genre | Science |
ISBN | 1489967982 |
Based on course material used by the author at Yale University, this practical text addresses the widening gap found between the mathematics required for upper-level courses in the physical sciences and the knowledge of incoming students. This superb book offers students an excellent opportunity to strengthen their mathematical skills by solving various problems in differential calculus. By covering material in its simplest form, students can look forward to a smooth entry into any course in the physical sciences.
School of Mathematics Program Review
Title | School of Mathematics Program Review PDF eBook |
Author | University of Minnesota. School of Mathematics |
Publisher | |
Pages | 435 |
Release | 1984 |
Genre | Mathematics |
ISBN |
Hands-On Mathematics for Deep Learning
Title | Hands-On Mathematics for Deep Learning PDF eBook |
Author | Jay Dawani |
Publisher | Packt Publishing Ltd |
Pages | 347 |
Release | 2020-06-12 |
Genre | Computers |
ISBN | 183864184X |
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.