In an increasingly electronic society, these exercises are designed to help school and collegiate educators use historical devices of mathematics to balance the digital side of mathematics.

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.

This teacher resource offers a detailed introduction to the Hands-On Mathematics program (guiding principles, implementation guidelines, an overview of the processes that grade 1 students use and develop during mathematics inquiry), and a classroom assessment plan complete with record-keeping templates and connections to the Achievement Levels outlined in the Ontario Mathematics Curriculum. The resource also provides strategies and visual resources for developing students' mental math skills. The resource includes: Mental Math Strategies Unit 1: Patterning and Algebra Unit 2: Data Management and Probability Unit 3: Measurement Unit 4: Geometry and Spatial Sense Unit 5: Number Sense and Numeration Each unit is divided into lessons that focus on specific curricular expectations. Each lesson has materials lists, activity descriptions, questioning techniques, problem-solving examples, activity centre and extension ideas, assessment suggestions, activity sheets, and visuals required.

Provides literature-based activities for teaching math to students in grades one through three, each with activities, reproducible patterns, and recording sheets.

Math write and wipe book for toddlers ages 4+ Support your child’s educational journey with Spectrum’s dry erase Hands-On Preschool Math Workbook that teaches basic math skills to preschoolers. Pre k workbooks age 4-5 are a great way for preschoolers to learn basic skills such as numbers and counting, addition, subtraction, shapes, patterns, and more through a variety of dry erase and math manipulative toddler learning activities that are both fun AND educational! Why You’ll Love This Preschool Workbook Engaging and educational dry erase preschool learning activities. “Tracing numbers”, “Count and color”, and “Adding and subtracting with math manipulatives” are a few of the fun toddler learning activities that incorporate math into everyday settings to help inspire learning into your child’s classroom or homeschool curriculum. Testing progress along the way. An answer key is included in the back of the preschool activity book to track your child’s progress along the way before moving on to new and exciting math activities. Practically sized for every activity. The 96-page math preschool book is sized at about 8 3⁄4” x 11”—giving your child plenty of space to complete each exercise. About Spectrum For more than 20 years, Spectrum has provided solutions for parents who want to help their children get ahead, and for teachers who want their students to meet and exceed set learning goals—providing workbooks that are a great resource for both homeschooling and classroom curriculum. The Dry Erase Math Pre K Workbook Contains: Dry erase math activities Dry erase pen, panels, graphic organizers, and hands-on math manipulatives An answer key

This teacher resource offers a detailed introduction to the Hands-On Mathematics program (guiding principles, implementation guidelines, an overview of the processes that grade 2 students use and develop during mathematics inquiry), and a classroom assessment plan complete with record-keeping templates and connections to the Achievement Levels outlined in the Ontario Mathematics Curriculum. It also provides strategies and visual resources for developing students' mental math skills. Each unit is divided into lessons that focus on specific curricular expectations. Each lesson has materials lists, activity descriptions, questioning techniques, problem-solving examples, activity centre and extension ideas, assessment suggestions, activity sheets and visuals.--Portage & Main Press.

Math Art is a supplemental, arts-integrated mathematics curriculum. The purpose of Math Art is to help teachers introduce, reinforce, or expand upon the topics their students are required to learn. Furthermore, by blending the subjects of mathematics and art, Math Art is capable of motivating students, decreasing classroom discipline problems, increasing student retention of knowledge, and assisting the instruction of visual learners, kinesthetic learners, and English Language Learners. Each of the book's math activities require students to create an aesthetically-pleasing project that focuses on a fundamental or "broad" mathematical topic (perimeter, volume, symmetry, angles, etc.).