Math for Programmers: 3D Graphics, Machine Learning, and Simulations with Python

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This is a big book already, so there just wasn’t time or room to squeeze a meaningful introduction for these topics.

You can use this form of notation to talk about both algorithm runtime as well as memory consumed, and it gives you concrete methods of comparison, even in cases where you don't know the size of the input.But on the other hand, many people rightly point out that programming is simply a subset of computer science, which itself is a subset of mathematics. Complexity theory is the study of the amount of time and memory it takes for an algorithm to run as a function of the input size.

As a programmer, you will be set tasks and realize you will be asking is this a linear regression problem or a logistic regression problem. They can help you develop more efficient code, solve complex problems, and unveil the magic behind the digital world. Kneusel is a data scientist who builds deep-learning (AI) systems, as well as extensive experience with medical imaging and the development of medical devices. The figure on the cover of Math for Progammers is captioned Femme Laponne, or a woman from Lapp, now Sapmi, which includes parts of northern Norway, Sweden, Finland, and Russia.For now, I’ll focus on a more pragmatic kind of motivation−solving mathematical problems with software can make you a lot of money. The book starts by introducing 2D and 3D vector geometry, vector spaces, linear transformations, and matrices; these are the bread and butter of the subject of linear algebra. In part 2, it introduces calculus with a focus on a few particularly useful subjects for programmers: derivatives, gradients, Euler’s method, and symbolic evaluation. Maybe if I became a construction site surveyor as my trigonometry teacher suggested, I’d be using sines and cosines every day to earn my paycheck.

The position vector of the projectile changes over time due to its initial speed and the pull of gravity. Part 3 combines the ideas from the first two parts to introduce some important ideas in machine learning. I suggest you use Jupyter instead of Python interactive, but in any case, lines with >>> represent inputs and lines without represent outputs. Filled with graphics and more than 200 exercises and mini-projects, this book unlocks the door to interesting–and lucrative! Kneusel is also the author of Random Numbers and Computers (Springer 2018), in addition to Math for Deep Learning , Practical Deep Learning , and Strange Code —all published by No Starch Press.

If you want to start specializing in graphics or designing languages or algo, then, yeah, you'll need math, but at that point, you'll know what math.

Filled with lots of helpful graphics and more than 200 exercises and mini-projects, this book unlocks the door to interesting–and lucrative!In chapters 3 and 4, you learn how to use 3D vector math to turn 3D models into shaded 2D images like the ones in figure 1. The rich variety of Grasset de Saint-Sauveur’s collection reminds us vividly of how culturally apart the world’s towns and regions were just 200 years ago. shows how each of the three numbers tells you how far to go in one of three perpendicular directions. If you were less discerning, you could read every word of a textbook and solve all of its exercises, but it could take months of full-time study to do that!