Organized around the central concept of a vector space, the book includes numerous physical applications in the body of the text as well as many problems of a physical nature. It is also one of the purposes of this book to introduce the physicist to the language and style of mathematics as well as the content of those particular subjects with contemporary relevance in physics.Chapters 1 and 2 are devoted to the mathematics of classical physics. Chapters 3, 4 and 5 — the backbone of the book — cover the theory of vector spaces. Chapter 6 covers analytic function theory. In chapters 7, 8, and 9 the authors take up several important techniques of theoretical physics — the Green's function method of solving differential and partial differential equations, and the theory of integral equations. Chapter 10 introduces the theory of groups. The authors have included a large selection of problems at the end of each chapter, some illustrating or extending mathematical points, others stressing physical application of techniques developed in the text.Essentially self-contained, the book assumes only the standard undergraduate preparation in physics and mathematics, i.e. intermediate mechanics, electricity and magnetism, introductory quantum mechanics, advanced calculus and differential equations. The text may be easily adapted for a one-semester course at the graduate or advanced undergraduate level.

    Mitchell covers the field of machine learning, the study of algorithms that allow computer programs to automatically improve through experience and that automatically infer general laws from specific data.

    This work represents the views of a mathematician on the analogies between computing machines and the living human brain.

    Right?! Machine Learning is a branch of computer science that wants to stop programming computers using a detailed list of commands to follow blindly. Instead, their aim is to implement high-level routines that teach computers how to approach new and unknown problems – these are called algorithms. In practice, they want to give computers the ability to Learn and to Adapt. We can use these algorithms to obtain insights, recognize patterns and make predictions from data, images, sounds or videos we have never seen before – or even knew existed. Unfortunately, the true power and applications of today’s Machine Learning Algorithms remain deeply misunderstood by most people. Through this book I want fix this confusion, I want to shed light on the most relevant Machine Learning Algorithms used in the industry. I will show you exactly how each algorithm works, why it works and when you should use it. Supervised Learning Algorithms K-Nearest Neighbour Naïve Bayes Regressions Unsupervised Learning Algorithms: Support Vector Machines Neural Networks Decision Trees

    It was also a weapon.Simplicity isn’t just a design principle at Apple—it’s a value that permeates every level of the organization. The obsession with Simplicity is what separates Apple from other technology companies. It’s what helped Apple recover from near death in 1997 to become the most valuable company on Earth in 2011.Thanks to Steve Jobs’s uncompromising ways, you can see Simplicity in everything Apple does: the way it’s structured, the way it innovates, and the way it speaks to its customers.It’s by crushing the forces of Complexity that the company remains on its stellar trajectory.As ad agency creative director, Ken Segall played a key role in Apple’s resurrection, helping to create such critical marketing campaigns as Think different. By naming the iMac, he also laid the foundation for naming waves of i-products to come.Segall has a unique perspective, given his years of experience creating campaigns for other iconic tech companies, including IBM, Intel, and Dell. It was the stark contrast of Apple’s ways that made Segall appreciate the power of Simplicity—and inspired him to help others benefit from it.In Insanely Simple, you’ll be a fly on the wall inside a conference room with Steve Jobs, and on the receiving end of his midnight phone calls. You’ll understand how his obsession with Simplicity helped Apple perform better and faster, sometimes saving millions in the process. You’ll also learn, for example, how to:• Think Minimal: Distilling choices to a minimum brings clarity to a company and its customers—as Jobs proved when he replaced over twenty product models with a lineup of four.• Think Small: Swearing allegiance to the concept of “small groups of smart people” raises both morale and productivity.• Think Motion: Keeping project teams in constant motion focuses creative thinking on well-defined goals and minimizes distractions.• Think Iconic: Using a simple, powerful image to symbolize the benefit of a product or idea creates a deeper impression in the minds of customers.• Think War: Giving yourself an unfair advantage—using every weapon at your disposal—is the best way to ensure that your ideas survive unscathed.Segall brings Apple’s quest for Simplicity to life using fascinating (and previously untold) stories from behind the scenes. Through his insight and wit, you’ll discover how companies that leverage this power can stand out from competitors—and individuals who master it can become critical assets to their organizations.

    But how can you be the person who comes up with those ideas? In this revised and expanded edition of his groundbreaking Thinkertoys, creativity expert Michael Michalko reveals life-changing tools that will help you think like a genius. From the linear to the intuitive, this comprehensive handbook details ingenious creative-thinking techniques for approaching problems in unconventional ways. Through fun and thought-provoking exercises, you’ll learn how to create original ideas that will improve your personal life and your business life. Michalko’s techniques show you how to look at the same information as everyone else and see something different.  With hundreds of hints, tricks, tips, tales, and puzzles, Thinkertoys will open your mind to a world of innovative solutions to everyday and not-so-everyday problems.

    The material is reinforced with numerous examples to illustrate principles and imaginative, well-illustrated problems of varying degrees of difficulty. The book is committed to developing users' problem-solving skills. Features new "Photorealistc" figures (approximately 400) that have been rendered in often 3D photo quality detail to appeal to visual learners. Presents a thorough combination of both static and dynamic engineering mechanics theory and applications. Features a large variety of problem types from a broad range of engineering disciplines, stressing practical, realistic situations encountered in professional practice, varying levels of difficulty, and problems that involve solution by computer. For professionals in mechanical engineering, civil engineering, aeronautical engineering, and engineering mechanics careers.

    It offers targeted assessment, easy-to-follow instruction, hundreds of reinforcement activities, and simulated GED tests for all five GED subject areas: Test 1 Language Arts, Writing; Test 2 Social Studies; Test 3 Science; Test 4 Language Arts, Reading; Test 5 Mathematics. A half-length Pretest for each subject helps pinpoint strengths and weaknesses for targeting study. Clear instruction, followed by hundred of practice questions in official GED format help to build confidence. A Posttest for each subject is followed by a second Practice Test in official GED format to determine readiness for the actual test.

    Covey's 7 Habits of Highly Effective People continues to pass down knowledge thousands of years old to a modern audience. The titular 7 habits will help you become more effective in business and personal situations. This 3500+ word executive summary by Spry Summaries boils down the essential knowledge from the 10-million copy seller into a compact and easy to read package. Take the knowledge of generations with you on the go!

    For the first time, these books are available as a boxed, three-volume set. The handsome slipcase makes this set an ideal gift for the recent computer science graduate or professional programmer. Offering a description of classical computer science, this multi-volume work is a useful resource in programming theory and practice for students, researchers, and practitioners alike. For programmers, it offers cookbook solutions to their day-to-day problems.

    In How Not to Be Wrong, Jordan Ellenberg shows us how terribly limiting this view is: Math isn’t confined to abstract incidents that never occur in real life, but rather touches everything we do—the whole world is shot through with it.Math allows us to see the hidden structures underneath the messy and chaotic surface of our world. It’s a science of not being wrong, hammered out by centuries of hard work and argument. Armed with the tools of mathematics, we can see through to the true meaning of information we take for granted: How early should you get to the airport? What does “public opinion” really represent? Why do tall parents have shorter children? Who really won Florida in 2000? And how likely are you, really, to develop cancer?How Not to Be Wrong presents the surprising revelations behind all of these questions and many more, using the mathematician’s method of analyzing life and exposing the hard-won insights of the academic community to the layman—minus the jargon. Ellenberg chases mathematical threads through a vast range of time and space, from the everyday to the cosmic, encountering, among other things, baseball, Reaganomics, daring lottery schemes, Voltaire, the replicability crisis in psychology, Italian Renaissance painting, artificial languages, the development of non-Euclidean geometry, the coming obesity apocalypse, Antonin Scalia’s views on crime and punishment, the psychology of slime molds, what Facebook can and can’t figure out about you, and the existence of God.Ellenberg pulls from history as well as from the latest theoretical developments to provide those not trained in math with the knowledge they need. Math, as Ellenberg says, is “an atomic-powered prosthesis that you attach to your common sense, vastly multiplying its reach and strength.” With the tools of mathematics in hand, you can understand the world in a deeper, more meaningful way. How Not to Be Wrong will show you how.

    A fun and fascinating look at great scientific paradoxes.   Throughout history, scientists have come up with theories and ideas that just don't seem to make sense.  These we call paradoxes.  The paradoxes Al-Khalili offers are drawn chiefly from physics and astronomy and represent those that have stumped some of the finest minds.  For example, how can a cat be both dead and alive at the same time?  Why will Achilles never beat a tortoise in a race, no matter how fast he runs?  And how can a person be ten years older than his twin?   With elegant explanations that bring the reader inside the mind of those who've developed them, Al-Khalili helps us to see that, in fact, paradoxes can be solved if seen from the right angle.  Just as surely as Al-Khalili narrates the enduring fascination of these classic paradoxes, he reveals their underlying logic.  In doing so, he brings to life a select group of the most exciting concepts in human knowledge.  Paradox is mind-expanding fun.

    Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not.Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you're familiar with the R programming language, and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format.With this book, you'll learn:Why exploratory data analysis is a key preliminary step in data scienceHow random sampling can reduce bias and yield a higher quality dataset, even with big dataHow the principles of experimental design yield definitive answers to questionsHow to use regression to estimate outcomes and detect anomaliesKey classification techniques for predicting which categories a record belongs toStatistical machine learning methods that "learn" from dataUnsupervised learning methods for extracting meaning from unlabeled data

    The long-anticipated revision of this best-selling text offers the most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence. *NEW-Nontechnical learning material-Accompanies each part of the book. *NEW-The Internet as a sample application for intelligent systems-Added in several places including logical agents, planning, and natural language. *NEW-Increased coverage of material - Includes expanded coverage of: default reasoning and truth maintenance systems, including multi-agent/distributed AI and game theory; probabilistic approaches to learning including EM; more detailed descriptions of probabilistic inference algorithms. *NEW-Updated and expanded exercises-75% of the exercises are revised, with 100 new exercises. *NEW-On-line Java software. *Makes it easy for students to do projects on the web using intelligent agents. *A unified, agent-based approach to AI-Organizes the material around the task of building intelligent agents. *Comprehensive, up-to-date coverage-Includes a unified view of the field organized around the rational decision making pa

    Each expresses a perspective on the Sciences.