Math for Programmers: 3D graphics, machine learning, and simulations with Python

by Paul Orland

"A gentle introduction to some of the most useful mathematical concepts that should be in your developer toolbox." - Christopher Haupt, New Relic Explore important mathematical concepts through hands-on coding.  Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.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. Summary 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. About the technology Skip the mathematical 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! What's inside     Vector geometry for computer graphics     Matrices and linear transformations     Core concepts from calculus     Simulation and optimization     Image and audio processing     Machine learning algorithms for regression and classification About the reader For programmers with basic skills in algebra. About the author Paul Orland 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. Table of Contents 1 Learning math with code PART I - VECTORS AND GRAPHICS 2 Drawing with 2D vectors 3 Ascending to the 3D world 4 Transforming vectors and graphics 5 Computing transformations with matrices 6 Generalizing to higher dimensions 7 Solving systems of linear equations PART 2 - CALCULUS AND PHYSICAL SIMULATION 8 Understanding rates of change 9 Simulating moving objects 10 Working with symbolic expressions 11 Simulating force fields 12 Optimizing a physical system 13 Analyzing sound waves with a Fourier series PART 3 - MACHINE LEARNING APPLICATIONS 14 Fitting functions to data 15 Classifying data with logistic regression 16 Training neural networks

Genres: Programming, Mathematics, Computer Science, Technical, Computers, Coding, Science

687 pages, Kindle Edition

Published November 30, 2020

Reviews

[Gutye Da Onfro · Rating 5 out of 5]

it is a very good book, i love how Paul Orland takes different points of view.

[Rick Sam · Rating 2 out of 5]

Paul Orland could concise this work in 100-150 pages.
He uses verbose examples and programming exercises.

I would recommend Danka's work on this topic.

Mathematics of Machine Learning by Dr. Tivadar Danka

So tell me more, what is happening?

Even though, I went through lot of the machine learning mathematical material. Honestly, I could not recall them in an instant and failed interviews.

So - I am back to revise the content, refresh my understanding. Also if you are preparing or attending interviews. Do not get dis-hearted with failures.

The last interview was for an excellent Post-Doc position.
It focused on Research & Software Engineering.

In that position, the interviewer opened his Google docs.
He even asked me from Machine Learning.
He asked me few basic questions, I was surprised, that I was able to recall.

What does this book cover?

Mathematical Ideas
-Multidimensional spaces
-Spaces of Function
-Derivates and Gradients
-Optimizing a function
-Predicting data with functions
-Calculus and Physical Simulation
-Machine Learning Applications

Vectors, Scalars, Gradient: Gives rate of change in every direction for e [e is unit vector]

Inner product:
Dot-product or Scalar product, remember as scalar product.
In ML papers, you would notice, < > symbol
Reduces dimensions

Outer product:
Also called as cross-product
When we take outer-product of two column vectors;
u ⊗ v, We get matrix.
Increases dimensions

And so on..

Deus Vult,
Gottfried

[William Schram · Rating 4 out of 5]

Mathematics has a bad reputation for many people. It's hard to learn, it's hard to teach, and it doesn't have applications in the real world. I assume they mean that they will not use it.

Author Paul Orland teaches mathematics in a fun and interactive manner with programming. Orland uses Python to do all of the book's projects. He feels that teachers impart math incorrectly. Mathematicians do not discover math the way they teach it.

Math for Programmers teaches linear algebra, calculus, and machine learning applications. It does so by helping the reader develop a game. The game looks similar to Asteroids by Atari.

Linear algebra allows us to draw the figures and do transformations on them. With calculus, we can apply gravity to our game and give our rocketship realistic physics. The machine learning segment covers optical character recognition and fitting data with linear regression.

The book expects you to have some programming experience but nothing too in-depth. It doesn't mention how much experience you need, but it does say you need a strong history of programming.

I enjoyed the book, but I should have followed along with the exercises. Thanks for reading my review, and see you next time.

[Putra · Rating 5 out of 5]

Would like to recommend this book to someone who want to see how math applied in computer science, Math seems fun again after reading this book! Thanks Paul!