Math for Programmers - by Paul Orland (Paperback)
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<p/><br></br><p><b> About the Book </b></p></br></br>"To score a job in data science, machine learning, computer graphics, and cryptography, you need to bring strong math skills to the party. Math for Programmers teaches the math you need for these hot careers, concentrating on what you need to know as a developer. Filled with lots of helpful graphics and more than 200 exercises and mini-projects, this book unlocks the door to interesting-and lucrative!--careers in some of today's hottest programming fields."--Publsher's description.<p/><br></br><p><b> Book Synopsis </b></p></br></br><b><b>Explore important mathematical concepts through hands-on coding. </b><br> <i><b>Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.</b></i><br> </b> <p/>To score a job in data science, machine learning, computer graphics, and cryptography, you need to bring strong math skills to the party. <i>Math for Programmers</i> teaches the math you need for these hot careers, concentrating on what you need to know as a developer. Filled with lots of helpful graphics and more than 200 exercises and mini-projects, this book unlocks the door to interesting-and lucrative!-careers in some of today's hottest programming fields. <p/><b>About the technology</b><br> Skip the mathematical jargon: This one-of-a-kind book uses Python to teach the math you need to build games, simulations, 3D graphics, and machine learning algorithms. Discover how algebra and calculus come alive when you see them in code! <p/> <b>About the book</b><br> In <i>Math for Programmers</i> you'll explore important mathematical concepts through hands-on coding. Filled with graphics and more than 300 exercises and mini-projects, this book unlocks the door to interesting-and lucrative!-careers in some of today's hottest fields. As you tackle the basics of linear algebra, calculus, and machine learning, you'll master the key Python libraries used to turn them into real-world software applications. <p/> <b>What's inside</b> <p/> Vector geometry for computer graphics<br> Matrices and linear transformations<br> Core concepts from calculus<br> Simulation and optimization<br> Image and audio processing<br> Machine learning algorithms for regression and classification <p/> <b>About the reader</b><br> For programmers with basic skills in algebra. <p/> <b>About the author</b><br> <b>Paul Orland</b> is a programmer, software entrepreneur, and math enthusiast. He is co-founder of Tachyus, a start-up building predictive analytics software for the energy industry. You can find him online at www.paulor.land. <p/> <b>Table of Contents</b> <p/> 1 Learning math with code <p/> PART I - VECTORS AND GRAPHICS <p/> 2 Drawing with 2D vectors <p/> 3 Ascending to the 3D world <p/> 4 Transforming vectors and graphics <p/> 5 Computing transformations with matrices <p/> 6 Generalizing to higher dimensions <p/> 7 Solving systems of linear equations <p/> PART 2 - CALCULUS AND PHYSICAL SIMULATION <p/> 8 Understanding rates of change <p/> 9 Simulating moving objects <p/> 10 Working with symbolic expressions <p/> 11 Simulating force fields <p/> 12 Optimizing a physical system <p/> 13 Analyzing sound waves with a Fourier series <p/> PART 3 - MACHINE LEARNING APPLICATIONS <p/> 14 Fitting functions to data <p/> 15 Classifying data with logistic regression <p/> 16 Training neural networks<p/><br></br><p><b> About the Author </b></p></br></br><b>Paul Orland</b> is CEO of Tachyus, a Silicon Valley startup building predictive analytics software to optimize energy production in the oil and gas industry. As founding CTO, 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.
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