Table of Contents
Math for Programmers - 3D graphics, machine learning, and simulations with Python by Paul Orland
Return to Math for Programmers, Math, Math for Data Science and DataOps, Math for Machine Learning and MLOps, Math for Programmers and Software Engineering, Outline of Mathematics, Math Bibliography, Outline of Software Engineering, Outline of Computer Science
Book Summary
Reviews
“A gentle introduction to some of the most useful mathematical concepts that should be in your developer toolbox.” –Christopher Haupt, New Relic
“A rigorous yet approachable overview of the mathematics that underpin a number of modern programming domains.” –Dan Sheikh, BCG Digital Ventures
“Engaging, practical, recommend for all levels.” –Vincent Zhu, rethinkxsocial.com
“It provides a bridge for programmers who need to brush up on their math skills, and does a nice job of making the math less mysterious and more approachable.” –Robert Walsh, Excalibur Solutions
About the Author
Paul Orland is a programmer, software entrepreneur, and math enthusiast. He is the co-founder of Tachyus, a Silicon Valley startup building predictive analytics software to optimize energy production in the oil and gas industry. At Tachyus he led the engineering team to productize hybrid machine learning and physics models, distributed optimization algorithms, and custom web-based data visualizations. He has a B.S. in mathematics from Yale University and a M.S. in physics from the University of Washington.
Product Details
- Publication date: January 12, 2021
- Paperback: 688 pages
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Math: Outline of mathematics, Mathematics research, Mathematical anxiety, Pythagorean Theorem, Scientific Notation, Algebra (Pre-algebra, Elementary algebra, Abstract algebra, Linear algebra, Universal algebra), Arithmetic (Essence of arithmetic, Elementary arithmetic, Decimal arithmetic, Decimal point, numeral system, Place value, Face value), Applied mathematics, Binary operation, Classical mathematics, Control theory, Cryptography, Definitions of mathematics, Discrete mathematics (Outline of discrete mathematics, Combinatorics), Dynamical systems, Engineering mathematics, Financial mathematics, Fluid mechanics (Mathematical fluid dynamics), Foundations of mathematics, Fudge (Mathematical fudge, Renormalization), Game theory, Glossary of areas of mathematics, Graph theory, Graph operations, Information theory, Language of mathematics, Mathematical economics, Mathematical logic (Model theory, Proof theory, Set theory, Type theory, Recursion theory, Theory of Computation, List of logic symbols), Mathematical optimization, Mathematician, Modulo, Mathematical notation (List of logic symbols, Notation in probability and statistics, Physical constants, Mathematical alphanumeric symbols, ISO 31-11), Numerical analysis, Operations research, Philosophy of mathematics, Probability (Outline of probability), Statistics, Mathematical structure, Ternary operation, Unary operation, Variable (mathematics), Glossary, Bibliography (Math for Data Science and DataOps, Math for Machine Learning and MLOps, Math for Programmers and Software Engineering), Courses, Mathematics GitHub. (navbar_math - see also navbar_variables)
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