Book picks similar to
The Cambridge Handbook of Physics Formulas by Graham Woan
physics
reference
mathematics
science
All the Math You'll Ever Need: A Self-Teaching Guide
Stephen L. Slavin - 1989
In adollars-and-cents, bottom-line world, where numbers influenceeverything, none of us can afford to let our math skills atrophy.This step-by-step personal math trainer:Refreshes practical math skills for your personal andprofessional needs, with examples based on everyday situations. Offers straightforward techniques for working with decimals and fractions. Demonstrates simple ways to figure discounts, calculatemortgage interest rates, and work out time, rate, and distance problems. Contains no complex formulas and no unnecessary technical terms.
The Nature of Space and Time
Stephen Hawking - 1996
But was he right? Can the quantum theory of fields and Einstein's general theory of relativity, the two most accurate and successful theories in all of physics, be united in a single quantum theory of gravity? Can quantum and cosmos ever be combined? On this issue, two of the world's most famous physicists--Stephen Hawking ("A Brief History of Time") and Roger Penrose ("The Emperor's New Mind" and "Shadows of the Mind")--disagree. Here they explain their positions in a work based on six lectures with a final debate, all originally presented at the Isaac Newton Institute for Mathematical Sciences at the University of Cambridge.How could quantum gravity, a theory that could explain the earlier moments of the big bang and the physics of the enigmatic objects known as black holes, be constructed? Why does our patch of the universe look just as Einstein predicted, with no hint of quantum effects in sight? What strange quantum processes can cause black holes to evaporate, and what happens to all the information that they swallow? Why does time go forward, not backward?In this book, the two opponents touch on all these questions. Penrose, like Einstein, refuses to believe that quantum mechanics is a final theory. Hawking thinks otherwise, and argues that general relativity simply cannot account for how the universe began. Only a quantum theory of gravity, coupled with the no-boundary hypothesis, can ever hope to explain adequately what little we can observe about our universe. Penrose, playing the realist to Hawking's positivist, thinks that the universe is unbounded and will expand forever. The universe can be understood, he argues, in terms of the geometry of light cones, the compression and distortion of spacetime, and by the use of twistor theory. With the final debate, the reader will come to realize how much Hawking and Penrose diverge in their opinions of the ultimate quest to combine quantum mechanics and relativity, and how differently they have tried to comprehend the incomprehensible.
Introduction to Topology
Bert Mendelson - 1975
It provides a simple, thorough survey of elementary topics, starting with set theory and advancing to metric and topological spaces, connectedness, and compactness. 1975 edition.
Hands-On Machine Learning with Scikit-Learn and TensorFlow
Aurélien Géron - 2017
Now that machine learning is thriving, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how.By using concrete examples, minimal theory, and two production-ready Python frameworks—Scikit-Learn and TensorFlow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn how to use a range of techniques, starting with simple Linear Regression and progressing to Deep Neural Networks. If you have some programming experience and you’re ready to code a machine learning project, this guide is for you.This hands-on book shows you how to use:Scikit-Learn, an accessible framework that implements many algorithms efficiently and serves as a great machine learning entry pointTensorFlow, a more complex library for distributed numerical computation, ideal for training and running very large neural networksPractical code examples that you can apply without learning excessive machine learning theory or algorithm details
Introduction to Mathematical Statistics
Robert V. Hogg - 1962
Designed for two-semester, beginning graduate courses in Mathematical Statistics, and for senior undergraduate Mathematics, Statistics, and Actuarial Science majors, this text retains its ongoing features and continues to provide students with background material.
Introductory Astronomy and Astrophysics
Michael Zeilik - 1987
It has an algebra and trigonometry prerequisite, but calculus is preferred.