This is a list of texts – non-traditional-book text, such as blog posts, papers, short e-books – I read and I thought interesting. The main purpose of this post isDavid Rönnqvist for my sake to remember things I read. This is a living post and it will grow as I keep reading.

## General

- The Mathematics of the 3D Rotation Matrix by Diana Gruber
- ThinkOS by Allen Downey
- How I learned to program by Dan Luu
- This VR cycle is dead by TechCrunch
- Network protocolsFor programmers who know at least one programming language by Gary Bernhardt
- Mind map everything by Nikita Voloboev
- [OpenGL Projection Matrix

](http://www.songho.ca/opengl/gl_projectionmatrix.html) by Songho

## ARkit

- Why is ARKit better than the alternatives? by Matt Miesnieks
- ARKit by Example — Part 4: Realism - Lighting & PBR by Mark Dawson
- Scroll by Nat Martin
- Custom SceneKit geometry by David Rönnqvist
- AR/VR Creator tools, smartglasses, AR toys and HoloLens mind mapping demoed at October’s ARBA by SuperVentures
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## Studis

- Master Thesis
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## iOS

### Autolayout

- How to use Auto Layout for iOS apps in Xcode 8
- How to unit test your Realm database layer by Hoang Tran
- THE RIGHT WAY TO WRITE A SINGLETON by Hector Matos

## Python

- matplotlib - 2D and 3D plotting in Python
- A report on a Kaggle competition
- Hands-On Machine Learning with Scikit-Learn and TensorFlow
- Python Testing with pytest: Simple, Rapid, Effective, and Scalable
- Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython
- Automate the Boring Stuff with Python: Practical Programming for Total Beginners

## Machine Learning

- A Fixed-Point of View on Gradient Methods for Big Data by Alexander Jung
- On Clustering Using Random Walks by David Harel and Yehuda Koren
- Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers by Stephen Boyd, Neal Parikh, Eric Chu, Borja Peleato and Jonathan Eckstein
- Support Vector Machine Solvers
- Learning From Labeled and Unlabeled data with label propagation by Carneige Mellon Univ.
- Learning Deep Architectures for AI by Yoshua Bengio
- A Few Useful Things to Know about Machine Learning by Pedro Domingos
- Kernel Methods in Computer Vision by Christoph H. Lampert
- A Tutorial on Spectral Clustering
- ML text book by MIT
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## Arts & Design

- 100 color combinations by Canva
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## Linear Algebra

- Essence of linear algebra by 3Blue1Brown
- immersive linear algebra