Algebra A Complete Introduction

Written by a leading expert, this book will help you if you are studying for an important exam or essay, or if you simply want to improve your knowledge.

Algebra  A Complete Introduction

Author: Hugh Neill

Publisher: Hachette UK

ISBN: 1473678420

Page: 400

View: 110

Algebra: A Complete Introduction is the most comprehensive yet easy-to-use introduction to using Algebra. Written by a leading expert, this book will help you if you are studying for an important exam or essay, or if you simply want to improve your knowledge. The book covers all the key areas of algebra including elementary operations, linear equations, formulae, simultaneous equations, quadratic equations, logarithms, variation, laws and sequences. Everything you will need is here in this one book. Each chapter includes not only an explanation of the knowledge and skills you need, but also worked examples and test questions. Chapter 1: The meaning of algebra Chapter 2: Elementary operations in algebra Chapter 3: Brackets and operations with them Chapter 4: Positive and negative numbers Chapter 5: Equations and expressions Chapter 6: Linear equations Chapter 7: Formulae Chapter 8: Simultaneous equations Chapter 9: Linear inequalities Chapter 10: Straight-line graphs; coordinates Chapter 11: Using inequalities to define regions Chapter 12: Multiplying algebraical expressions Chapter 13: Factors Chapter 14: Fractions Chapter 15: Graphs of quadratic functions Chapter 16: Quadratic equations Chapter 17: Indices Chapter 18: Logarithms Chapter 19: Ratio and proportion Chapter 20: Variation Chapter 21: The determination of laws Chapter 22: Rational and irrational numbers and surds Chapter 23: Arithmetical and geometric sequences

Algebra

A comprehensive yet easy-to-use introduction to using algebra. This book covers all the key areas of algebra, and is useful in studying for an exam or if you simply want to improve your knowledge.

Algebra

Author: Paul Abbott (Mathematics teacher)

Publisher:

ISBN: 9781784025182

Page:

View: 479

A comprehensive yet easy-to-use introduction to using algebra. This book covers all the key areas of algebra, and is useful in studying for an exam or if you simply want to improve your knowledge.

Algebra

The book covers all the key areas of algebra including elementary operations, linear equations, formulae, simultaneous equations, quadratic equations, logarithms, variation, law and sequences.

Algebra

Author: Hugh Neill

Publisher: Teach Yourself

ISBN: 9781473678415

Page: 388

View: 196

Algebra: A Complete Introduction is the most comprehensive yet easy-to-use introduction to using Algebra. Written by a leading expert, this book will help you if you are studying for an important exam or essay, or if you simply want to improve your knowledge. The book covers all the key areas of algebra including elementary operations, linear equations, formulae, simultaneous equations, quadratic equations, logarithms, variation, law and sequences. Everything you will need is here in this one book. Each chapter includes not only an explanation of the knowledge and skills you need, but also worked examples and test questions.

Complete Mathematics A Teach Yourself Guide

More than 60 million Teach Yourself products sold worldwide!

Complete Mathematics  A Teach Yourself Guide

Author: Trevor Johnson

Publisher: McGraw-Hill Professional

ISBN: 0071754571

Page: 336

View: 819

More than 60 million Teach Yourself products sold worldwide! Brush up your skills and rediscover the key math concepts and techniques Complete Mathematics provides an invaluable, step-by-step introduction to the subject. Packed full of worked examples and useful exercises, it will guide you through the essentials quickly and easily, giving you the knowledge you need to gain math confidence. Includes: One, five and ten-minute introductions to key principles to get you started. Lots of instant help with common problems and quick tips for success, based on the author’s many years of experience. Tests to keep track of one's progress. Worked examples, clear explanations and exercises. Topics include: Number; Angles; Fractions; Two-dimensional shapes; Decimals; Statistics; Directed numbers; Graphs; Measurement; Perimeter and area; Algebra; Approximation; Equations; Percentages; Formulae; Circles; Probability; Three-dimensional shapes; Ratio and proportion; Pythagoras' Theorem and trigonometry; Indices and standard form; Statistics

Kant A Complete Introduction Teach Yourself

... philosophers regardedtheideaof rationality as best expressedbythe discipline
of formallogic–adiscipline that Aristotle firstformulated which, like algebra,
usesletters and symbolstorefer toandtodesignate abstractconnections
betweenconcepts, ...

Kant  A Complete Introduction  Teach Yourself

Author: Robert Wicks

Publisher: Hachette UK

ISBN: 1444191284

Page: 288

View: 257

Written by Robert Wicks, a recognised Kant specialist who teaches at the University of Auckland, Kant: A Complete Introduction is designed to give you everything you need to succeed, all in one place. It covers the key areas that students are expected to be confident in, outlining the basics in clear jargon-free English, and then providing added-value features like summaries of key books, and even lists of questions you might be asked in your seminar or exam. The book uses a structure that mirrors many university courses on Freud and psychoanalysis - explaining and contextualising Kant's theories, which have been among the most influential in Philosophy. The book starts by introducing Kant and his way of thinking and arguing, before looking at how Kant answered three key questions: What can I know? What should I do? What may I hope? In doing so, Professor Wicks introduces the reader to all of Kant's key work, including The Critique of Pure Reason. Teach Yourself titles employ the 'Breakthrough method', which is designed specifically to overcome problems that students face. - Problem: "I find it difficult to remember what I've read."; Solution: this book includes end-of-chapter questions and summaries, and flashcards of key points available on-line and as apps - Problem: "Most books mention important other sources, but I can never find them in time."; Solution: this book includes key texts and case studies are summarised, complete with fully referenced quotes ready to use in your essay or exam. - Problem: "Lots of introductory books turn out to cover totally different topics than my course."; Solution: this book is written by a current university lecturer who understands what students are expected to know.

Patrick Moore s Astronomy A Complete Introduction Teach Yourself

This is a book about astronomy, not space research, but the two are now so
closely intertwined that it is impossible to separate them, any more than one can
separate arithmetic from algebra. I felt therefore that it was essential to say at
least a ...

Patrick Moore s Astronomy  A Complete Introduction  Teach Yourself

Author: Patrick Moore

Publisher: Hachette UK

ISBN: 1473608767

Page: 288

View: 530

Astronomy: A Complete Introduction will ensure you recognize what you are seeing in the night sky. You will investigate the sun, moon, planets comets and stars and learn how to observe them. This comprehensive guide, complete with star charts, will map out the skies and allow you to impress your friends with your knowledge of the sky at night. Astronomy: A Complete Introduction includes: Chapter 1: Introducing Astronomy Chapter 2: The spinning sky Chapter 3: Sky-watchers Chapter 4: The astronomer's telescope Chapter 5: Into space Chapter 6: The Sun Chapter 7: The Moon Chapter 8: The Sun's family Chapter 9: The inner planets Chapter 10: The outer planets Chapter 11: Minor members of the Solar System Chapter 12: The stars Chapter 13: Pattern of stars Chapter 14: Double and variable stars Chapter 15: The life and times of a star Chapter 16: The Star-clusters and nebulae Chapter 17: The depths of the universe Chapter 18: Into the future - life beyond the Earth

Statistics A Complete Introduction

The book covers all the key areas of Statistics including graphs, data interpretation, spreadsheets, regression, correlation and probability. Everything you will need is here in this one book.

Statistics   A Complete Introduction

Author: Alan Graham

Publisher: Teach Yourself

ISBN: 9781444191172

Page: 368

View: 583

Statistics: A Complete Introduction is the most comprehensive yet easy-to-use introduction to using Statistics. Written by a leading expert, this book will help you if you are studying for an important exam or essay, or if you simply want to improve your knowledge. The book covers all the key areas of Statistics including graphs, data interpretation, spreadsheets, regression, correlation and probability. Everything you will need is here in this one book. Each chapter includes not only an explanation of the knowledge and skills you need, but also worked examples and test questions.

Practical Algebra

Practical Algebra If you studied algebra years ago and now need arefresher course in order to use algebraic principles on the job,or if you're a student who needs an introduction to the subject,here's the perfect book for you.

Practical Algebra

Author: Peter H. Selby

Publisher: John Wiley & Sons

ISBN: 0471530123

Page: 336

View: 616

Practical Algebra If you studied algebra years ago and now need arefresher course in order to use algebraic principles on the job,or if you're a student who needs an introduction to the subject,here's the perfect book for you. Practical Algebra is an easy andfun-to-use workout program that quickly puts you in command of allthe basic concepts and tools of algebra. With the aid of practical,real-life examples and applications, you'll learn: * The basic approach and application of algebra to problemsolving * The number system (in a much broader way than you have known itfrom arithmetic) * Monomials and polynomials; factoring algebraic expressions; howto handle algebraic fractions; exponents, roots, and radicals;linear and fractional equations * Functions and graphs; quadratic equations; inequalities; ratio,proportion, and variation; how to solve word problems, andmore Authors Peter Selby and Steve Slavin emphasize practical algebrathroughout by providing you with techniques for solving problems ina wide range of disciplines--from engineering, biology, chemistry,and the physical sciences, to psychology and even sociology andbusiness administration. Step by step, Practical Algebra shows youhow to solve algebraic problems in each of these areas, then allowsyou to tackle similar problems on your own, at your own pace.Self-tests are provided at the end of each chapter so you canmeasure your mastery.

Linear Algebra A Modern Introduction

Balancing theory and applications, the book is written in a conversational style and combines a traditional presentation with a focus on student-centered learning.

Linear Algebra  A Modern Introduction

Author: David Poole

Publisher: Cengage Learning

ISBN: 1285982835

Page: 720

View: 995

David Poole's innovative LINEAR ALGEBRA: A MODERN INTRODUCTION, 4e emphasizes a vectors approach and better prepares students to make the transition from computational to theoretical mathematics. Balancing theory and applications, the book is written in a conversational style and combines a traditional presentation with a focus on student-centered learning. Theoretical, computational, and applied topics are presented in a flexible yet integrated way. Stressing geometric understanding before computational techniques, vectors and vector geometry are introduced early to help students visualize concepts and develop mathematical maturity for abstract thinking. Additionally, the book includes ample applications drawn from a variety of disciplines, which reinforce the fact that linear algebra is a valuable tool for modeling real-life problems. Important Notice: Media content referenced within the product description or the product text may not be available in the ebook version.

Understand Trigonometry A Teach Yourself Guide

Tests in the book to keep track of your progress Questions accompanied by detailed answers which help you to identify your areas of strength and weakness Topics include: Historical background; The tangent; Sine and cosine; In three ...

Understand Trigonometry  A Teach Yourself Guide

Author: Paul Abbott

Publisher: McGraw-Hill

ISBN: 9780071754842

Page: 208

View: 156

More than 60 million Teach Yourself products sold worldwide! The definitive guide to the basics of trigonometry Whether you are new to trigonometry or just need to refresh your knowledge, Understand Trigonometry is the ideal handbook of this complex subject. Offering a complete introduction, with fully worked examples and carefully paced exercises at every level, you will progress steadily from the basics to more advanced skills and gain new confidence in your abilities. One, five and ten-minute introductions to key principles to get you started Lots of instant help with common problems and quick tips for success, based on the author’s many years of experience. Tests in the book to keep track of your progress Questions accompanied by detailed answers which help you to identify your areas of strength and weakness Topics include: Historical background; The tangent; Sine and cosine; In three dimensions; Angles of any magnitude; Solving simple equations; The sine and cosine formulae; Radians; Relations between the ratios; Ratios of compound angles; The form asinx+bcosx; The factor formulae; Circles related to a triangle; General solution of equations

Linear Algebra and Optimization for Machine Learning

This textbook introduces linear algebra and optimization in the context of machine learning. Examples and exercises are provided throughout this text book together with access to a solution’s manual.

Linear Algebra and Optimization for Machine Learning

Author: Charu C. Aggarwal

Publisher: Springer Nature

ISBN: 3030403440

Page: 495

View: 605

This textbook introduces linear algebra and optimization in the context of machine learning. Examples and exercises are provided throughout this text book together with access to a solution’s manual. This textbook targets graduate level students and professors in computer science, mathematics and data science. Advanced undergraduate students can also use this textbook. The chapters for this textbook are organized as follows: 1. Linear algebra and its applications: The chapters focus on the basics of linear algebra together with their common applications to singular value decomposition, matrix factorization, similarity matrices (kernel methods), and graph analysis. Numerous machine learning applications have been used as examples, such as spectral clustering, kernel-based classification, and outlier detection. The tight integration of linear algebra methods with examples from machine learning differentiates this book from generic volumes on linear algebra. The focus is clearly on the most relevant aspects of linear algebra for machine learning and to teach readers how to apply these concepts. 2. Optimization and its applications: Much of machine learning is posed as an optimization problem in which we try to maximize the accuracy of regression and classification models. The “parent problem” of optimization-centric machine learning is least-squares regression. Interestingly, this problem arises in both linear algebra and optimization, and is one of the key connecting problems of the two fields. Least-squares regression is also the starting point for support vector machines, logistic regression, and recommender systems. Furthermore, the methods for dimensionality reduction and matrix factorization also require the development of optimization methods. A general view of optimization in computational graphs is discussed together with its applications to back propagation in neural networks. A frequent challenge faced by beginners in machine learning is the extensive background required in linear algebra and optimization. One problem is that the existing linear algebra and optimization courses are not specific to machine learning; therefore, one would typically have to complete more course material than is necessary to pick up machine learning. Furthermore, certain types of ideas and tricks from optimization and linear algebra recur more frequently in machine learning than other application-centric settings. Therefore, there is significant value in developing a view of linear algebra and optimization that is better suited to the specific perspective of machine learning.

Linear Algebra and Learning from Data

From Professor Gilbert Strang, acclaimed author of Introduction to Linear Algebra, comes Linear Algebra and Learning from Data, the first textbook that teaches linear algebra together with deep learning and neural nets.

Linear Algebra and Learning from Data

Author: Gilbert Strang

Publisher: Wellesley-Cambridge Press

ISBN: 9780692196380

Page: 446

View: 959

Linear algebra and the foundations of deep learning, together at last! From Professor Gilbert Strang, acclaimed author of Introduction to Linear Algebra, comes Linear Algebra and Learning from Data, the first textbook that teaches linear algebra together with deep learning and neural nets. This readable yet rigorous textbook contains a complete course in the linear algebra and related mathematics that students need to know to get to grips with learning from data. Included are: the four fundamental subspaces, singular value decompositions, special marices, large matrix computation techniques, compressed sensing, probability and statistics, optimization, the architecture of neural nets, stochastic gradient descent and backpropagation.

Linear Algebra

Praise for the Third Edition "This volume is ground-breaking in terms of mathematical texts in that it does not teach from a detached perspective, but instead, looks to show students that competent mathematicians bring an intuitive ...

Linear Algebra

Author: Richard C. Penney

Publisher: John Wiley & Sons

ISBN: 1118909623

Page: 512

View: 374

Praise for the Third Edition “This volume is ground-breaking in terms of mathematical texts in that it does not teach from a detached perspective, but instead, looks to show students that competent mathematicians bring an intuitive understanding to the subject rather than just a master of applications.” – Electric Review A comprehensive introduction, Linear Algebra: Ideas and Applications, Fourth Edition provides a discussion of the theory and applications of linear algebra that blends abstract and computational concepts. With a focus on the development of mathematical intuition, the book emphasizes the need to understand both the applications of a particular technique and the mathematical ideas underlying the technique. The book introduces each new concept in the context of an explicit numerical example, which allows the abstract concepts to grow organically out of the necessity to solve specific problems. The intuitive discussions are consistently followed by rigorous statements of results and proofs. Linear Algebra: Ideas and Applications, Fourth Edition also features: Two new and independent sections on the rapidly developing subject of wavelets A thoroughly updated section on electrical circuit theory Illuminating applications of linear algebra with self-study questions for additional study End-of-chapter summaries and sections with true-false questions to aid readers with further comprehension of the presented material Numerous computer exercises throughout using MATLAB® code Linear Algebra: Ideas and Applications, Fourth Edition is an excellent undergraduate-level textbook for one or two semester courses for students majoring in mathematics, science, computer science, and engineering. With an emphasis on intuition development, the book is also an ideal self-study reference.

Introduction to Statistical Machine Learning

Introduction to Statistical Machine Learning provides a general introduction to machine learning that covers a wide range of topics concisely and will help you bridge the gap between theory and practice.

Introduction to Statistical Machine Learning

Author: Masashi Sugiyama

Publisher: Morgan Kaufmann

ISBN: 0128023503

Page: 534

View: 341

Machine learning allows computers to learn and discern patterns without actually being programmed. When Statistical techniques and machine learning are combined together they are a powerful tool for analysing various kinds of data in many computer science/engineering areas including, image processing, speech processing, natural language processing, robot control, as well as in fundamental sciences such as biology, medicine, astronomy, physics, and materials. Introduction to Statistical Machine Learning provides a general introduction to machine learning that covers a wide range of topics concisely and will help you bridge the gap between theory and practice. Part I discusses the fundamental concepts of statistics and probability that are used in describing machine learning algorithms. Part II and Part III explain the two major approaches of machine learning techniques; generative methods and discriminative methods. While Part III provides an in-depth look at advanced topics that play essential roles in making machine learning algorithms more useful in practice. The accompanying MATLAB/Octave programs provide you with the necessary practical skills needed to accomplish a wide range of data analysis tasks. Provides the necessary background material to understand machine learning such as statistics, probability, linear algebra, and calculus. Complete coverage of the generative approach to statistical pattern recognition and the discriminative approach to statistical machine learning. Includes MATLAB/Octave programs so that readers can test the algorithms numerically and acquire both mathematical and practical skills in a wide range of data analysis tasks Discusses a wide range of applications in machine learning and statistics and provides examples drawn from image processing, speech processing, natural language processing, robot control, as well as biology, medicine, astronomy, physics, and materials.

Machine Learning Math

Machine Learning Math is the book most readers will want to have when starting to learn machine learning. This book is a reference, something you can keep coming back to hence suitable for newbies.

Machine Learning Math

Author:

Publisher:

ISBN:

Page: 228

View: 665

Are you looking for a complete guide of machine learning? Then keep reading... In this book, you will learn about the OpenAI Gym, used in reinforcement learning projects with several examples of the training platform provided out of the box. Machine Learning Math is the book most readers will want to have when starting to learn machine learning. This book is a reference, something you can keep coming back to hence suitable for newbies. The book is perfect for all people who have a desire to study data science. Have you heard of machine learning being everywhere, and you intend to understand what it can do? Or are you familiar with applying the tools of machine learning, but you want to make sure you aren't missing any? Having a little knowledge about mathematics, statistics, and probability would be helpful, but this book has been written in such a way that you will get most of this knowledge as you continue reading. You should not shy away from reading the book if you have no background in machine learning. You will learn how to use reinforcement learning algorithms in other tasks, for example, the board game Go, and generating deep image classifiers. This will help you to get a comprehensive understanding of reinforcement learning and help you solve real-world problems. The most interesting part of this book is the asynchronous reinforcement learning framework. You will learn what the shortcomings of DQN are, and why DQN is challenging to apply in complex tasks. Then, you will learn how to apply the asynchronous reinforcement learning framework in the actor-critic method REINFORCE, which led us to the A3C algorithm. You will learn four important things. The first one is how to implement games using gym and how to play games for relaxation and having fun. The second one is that you will learn how to preprocess data in reinforcement learning tasks such as in computer games. For practical machine learning applications, you will spend a great deal of time understanding and refining data, which affects the performance of an AI system a lot. The third one is the deep Q-learning algorithm. You will learn the intuition behind it, for example, why the replay memory is necessary, why the target network is needed, where the update rule comes from, and so on. The final one is that you will learn how to implement DQN using TensorFlow and how to visualize the training process. The following is a glimpse of what you will find inside the book: Introduction to machine learning The best machine learning algorithms Regression (a problem of predicting a real-valued label) and classification( a problem of automatically assigning a label to unlabeled example-for example spam detection) Reinforcement learning Robotics Supervised and Unsupervised learning How to implement a convolutional neural network(usually used for images) in TensorFlow Deep Learning Data preparation and processing TensorFlow machine learning frameworks Neural Networks (a combination of linear and non-linear functions) Clustering(aims to group similar samples together) Even if you have never studied Machine Learning before, you can learn it quickly. So what are you waiting for? Go to the top of the page and click Buy Now!

Abstract Algebra An Introduction

New to this edition is a groups first option that enables those who prefer to cover groups before rings to do so easily.

Abstract Algebra  An Introduction

Author: Thomas W. Hungerford

Publisher: Cengage Learning

ISBN: 1285414977

Page: 616

View: 973

Abstract Algebra: An Introduction is set apart by its thematic development and organization. The chapters are organized around two themes: arithmetic and congruence. Each theme is developed first for the integers, then for polynomials, and finally for rings and groups. This enables students to see where many abstract concepts come from, why they are important, and how they relate to one another. New to this edition is a groups first option that enables those who prefer to cover groups before rings to do so easily. Important Notice: Media content referenced within the product description or the product text may not be available in the ebook version.

Introduction to Machine Learning

The book provides an extensive theoretical account of the fundamental ideas underlying machine learning and the mathematical derivations that transform these principles into practical algorithms.

Introduction to Machine Learning

Author: Aditya Tandon

Publisher: Book Bazooka

ISBN: 9386895714

Page: 153

View: 791

Machine learning is one of the fastest growing areas of computer science, with far-reaching applications. The aim of this textbook is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way. The book provides an extensive theoretical account of the fundamental ideas underlying machine learning and the mathematical derivations that transform these principles into practical algorithms. Following a presentation of the basics of the field, the book covers a wide array of central topics that have not been addressed by previous textbooks. These include a discussion of the computational complexity of learning and the concepts of convexity and stability; important algorithmic paradigms including stochastic gradient descent, neural networks, and structured output learning; and emerging theoretical concepts such as the PAC-Bayes approach and compression-based bounds. Designed for an advanced undergraduate or beginning graduate course, the text makes the fundamentals and algorithms of machine learning accessible to students and nonexpert readers in statistics, computer science, mathematics, and engineering.

Teaching and Learning Mathematics

Algebra: the. Transition. from. Arithmetic. Introduction The earliest stages in the
teaching and learning of arithmetic are ... experience early ideas in learning the
concept of fractions, they are being introduced to the 'part-whole' concept which
is ...

Teaching and Learning Mathematics

Author: Marilyn Nickson

Publisher: A&C Black

ISBN: 0826472370

Page: 226

View: 346

This is a summary of the research in all the major topics of interest and concern to teachers of mathematics, from primary (elementary) to secondary (high) schools. It is directed towards students, in-service teachers, maths advisers and tutors.

The Future of the Teaching and Learning of Algebra

It shows some of the differences in school structures that impinge on who learns
algebra and at what stage. It surveys briefly some of the ... There is no attempt
here to be comprehensive, but instead to point out some of the dimensions of
difference around the world. There are weak links between the ... Introduction.
Scope of this survey 13.1.1 As we have seen in various chapters throughout this
book ...

The Future of the Teaching and Learning of Algebra

Author: Kaye Stacey

Publisher: Springer Science & Business Media

ISBN: 1402081308

Page: 371

View: 299

This book presents a wide-ranging, international perspective on the state of the field of algebra from invited participants to the 12th ICMI Study Conference held in Melbourne, Australia in 2001. The authors are renowned academics from all around the world who have written individual chapters associated with the teaching and learning of algebra that relate to their particular areas of research and teaching expertise. The book includes information about different approaches to the teaching and learning of algebra - from early algebra to tertiary algebra, the impact of tools and technology (including Computer Algebra Systems), the role of symbols and language, teachers of algebra, and the history of algebra. The Future of the Teaching and Learning of Algebra: the 12th ICMI Study is of interest to researchers, curriculum developers, educational policy makers, teachers of mathematics, and trainee mathematics teachers.