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. Key Features · 2D and 3D vector math · Matrices and linear transformations · Core concepts from linear algebra · Calculus with one or more…mehr
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. Key Features · 2D and 3D vector math · Matrices and linear transformations · Core concepts from linear algebra · Calculus with one or more variables · Algorithms for regression, classification, and clustering · Interesting real-world examples Written for programmers with solid algebra skills (even if they need some dusting off). No formal coursework in linear algebra or calculus is required. About the technology Most businesses realize they need to apply data science and effective machine learning to gain and maintain a competitive edge. To build these applications, they need developers comfortable writing code and using tools steeped in statistics, linear algebra, and calculus. Math also plays an integral role in other modern applications like game development, computer graphics and animation, image and signal processing, pricing engines, and stock market analysis. Paul Orland 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.
Paul Orland 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.
Inhaltsangabe
table of contents READ IN LIVEBOOK1LEARNING MATH WITH CODE PART 1: VECTORS AND GRAPHICS READ IN LIVEBOOK2DRAWING WITH 2D VECTORS READ IN LIVEBOOK3ASCENDING TO THE 3D WORLD READ IN LIVEBOOK4TRANSFORMING VECTORS AND GRAPHICS READ IN LIVEBOOK5COMPUTING TRANSFORMATIONS WITH MATRICES READ IN LIVEBOOK6GENERALIZING TO HIGHER DIMENSIONS READ IN LIVEBOOK7SOLVING SYSTEMS OF LINEAR EQUATIONS PART 2: CALCULUS AND PHYSICAL SIMULATION READ IN LIVEBOOK8UNDERSTANDING RATES OF CHANGE READ IN LIVEBOOK9SIMULATING MOVING OBJECTS READ IN LIVEBOOK10WORKING WITH SYMBOLIC EXPRESSIONS READ IN LIVEBOOK11SIMULATING FORCE FIELDS READ IN LIVEBOOK12OPTIMIZING A PHYSICAL SYSTEM READ IN LIVEBOOK13ANALYZING SOUND WAVES WITH A FOURIER SERIES PART 3: MACHINE LEARNING APPLICATIONS READ IN LIVEBOOK14FITTING FUNCTIONS TO DATA READ IN LIVEBOOK15CLASSIFYING DATA WITH LOGISTIC REGRESSION READ IN LIVEBOOK16TRAINING NEURAL NETWORKS APPENDIXES READ IN LIVEBOOKAPPENDIX A: GETTING SET UP WITH PYTHON READ IN LIVEBOOKAPPENDIX B: PYTHON TIPS AND TRICKS READ IN LIVEBOOKAPPENDIX C: LOADING AND RENDERING 3D MODELS WITH OPENGL AND PYGAME
table of contents READ IN LIVEBOOK1LEARNING MATH WITH CODE PART 1: VECTORS AND GRAPHICS READ IN LIVEBOOK2DRAWING WITH 2D VECTORS READ IN LIVEBOOK3ASCENDING TO THE 3D WORLD READ IN LIVEBOOK4TRANSFORMING VECTORS AND GRAPHICS READ IN LIVEBOOK5COMPUTING TRANSFORMATIONS WITH MATRICES READ IN LIVEBOOK6GENERALIZING TO HIGHER DIMENSIONS READ IN LIVEBOOK7SOLVING SYSTEMS OF LINEAR EQUATIONS PART 2: CALCULUS AND PHYSICAL SIMULATION READ IN LIVEBOOK8UNDERSTANDING RATES OF CHANGE READ IN LIVEBOOK9SIMULATING MOVING OBJECTS READ IN LIVEBOOK10WORKING WITH SYMBOLIC EXPRESSIONS READ IN LIVEBOOK11SIMULATING FORCE FIELDS READ IN LIVEBOOK12OPTIMIZING A PHYSICAL SYSTEM READ IN LIVEBOOK13ANALYZING SOUND WAVES WITH A FOURIER SERIES PART 3: MACHINE LEARNING APPLICATIONS READ IN LIVEBOOK14FITTING FUNCTIONS TO DATA READ IN LIVEBOOK15CLASSIFYING DATA WITH LOGISTIC REGRESSION READ IN LIVEBOOK16TRAINING NEURAL NETWORKS APPENDIXES READ IN LIVEBOOKAPPENDIX A: GETTING SET UP WITH PYTHON READ IN LIVEBOOKAPPENDIX B: PYTHON TIPS AND TRICKS READ IN LIVEBOOKAPPENDIX C: LOADING AND RENDERING 3D MODELS WITH OPENGL AND PYGAME
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