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High school algebra, grades 9-12.
This is the first volume of a graduate-level textbook series in the area of Algebraic Quantum Symmetry. The focus of this book series is on how one can do abstract algebra in the setting of monoidal categories. It is intended for readers who are familiar with abstract vector spaces, groups, rings, and ideals, and the author takes care in introducing categorical concepts from scratch. This book series on Symmetries of Algebras is intended to serve as learning books to newcomers to the area of research, and a carefully curated list of additional textbooks and articles are featured at the end of each chapter for further exploration. There are also numerous exercises throughout the series, with close to 200 exercises in Volume 1 alone. If you enjoy algebra, and are curious about how it fits into a broader context, this is for you.
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.
This textbook intends to be a comprehensive and substantially self-contained two-volume book covering performance, reliability, and availability evaluation subjects. The volumes focus on computing systems, although the methods may also be applied to other systems. The first volume covers Chapter 1 to Chapter 14, whose subtitle is ``Performance Modeling and Background". The second volume encompasses Chapter 15 to Chapter 25 and has the subtitle ``Reliability and Availability Modeling, Measuring and Workload, and Lifetime Data Analysis". This text is helpful for computer performance professionals for supporting planning, design, configuring, and tuning the performance, reliability, and availability of computing systems. Such professionals may use these volumes to get acquainted with specific subjects by looking at the particular chapters. Many examples in the textbook on computing systems will help them understand the concepts covered in each chapter. The text may also be helpful for the instructor who teaches performance, reliability, and availability evaluation subjects. Many possible threads could be configured according to the interest of the audience and the duration of the course. Chapter 1 presents a good number of possible courses programs that could be organized using this text. Volume I is composed of the first two parts, besides Chapter 1. Part I gives the knowledge required for the subsequent parts of the text. This part includes six chapters. It covers an introduction to probability, descriptive statistics and exploratory data analysis, random variables, moments, covariance, some helpful discrete and continuous random variables, Taylor series, inference methods, distribution fitting, regression, interpolation, data scaling, distance measures, and some clustering methods. Part II presents methods for performance evaluation modeling, such as operational analysis, Discrete-Time Markov Chains (DTMC), and Continuous Time Markov Chains (CTMC), Markovian queues, Stochastic Petri nets (SPN), and discrete event simulation.
Covers a notably broad range of topics, including some topics not generally found in linear algebra books Contains a discussion of the basics of linear algebra
What activities might a teacher use to help children explore the life cycle of butterflies? What does a science teacher need to conduct a "leaf safari" for students? Where can children safely enjoy hands-on experience with life in an estuary? Selecting resources to teach elementary school science can be confusing and difficult, but few decisions have greater impact on the effectiveness of science teaching. Educators will find a wealth of information and expert guidance to meet this need in Resources for Teaching Elementary School Science. A completely revised edition of the best-selling resource guide Science for Children: Resources for Teachers, this new book is an annotated guide to hands-on, inquiry-centered curriculum materials and sources of help in teaching science from kindergarten through sixth grade. (Companion volumes for middle and high school are planned.) The guide annotates about 350 curriculum packages, describing the activities involved and what students learn. Each annotation lists recommended grade levels, accompanying materials and kits or suggested equipment, and ordering information. These 400 entries were reviewed by both educators and scientists to ensure that they are accurate and current and offer students the opportunity to: Ask questions and find their own answers. Experiment productively. Develop patience, persistence, and confidence in their own ability to solve real problems. The entries in the curriculum section are grouped by scientific areaâ€"Life Science, Earth Science, Physical Science, and Multidisciplinary and Applied Scienceâ€"and by typeâ€"core materials, supplementary materials, and science activity books. Additionally, a section of references for teachers provides annotated listings of books about science and teaching, directories and guides to science trade books, and magazines that will help teachers enhance their students' science education. Resources for Teaching Elementary School Science also lists by region and state about 600 science centers, museums, and zoos where teachers can take students for interactive science experiences. Annotations highlight almost 300 facilities that make significant efforts to help teachers. Another section describes more than 100 organizations from which teachers can obtain more resources. And a section on publishers and suppliers give names and addresses of sources for materials. The guide will be invaluable to teachers, principals, administrators, teacher trainers, science curriculum specialists, and advocates of hands-on science teaching, and it will be of interest to parent-teacher organizations and parents.