Multithreaded parallelism in Numpy
Numpy uses BLAS, a linear algebra library which actually uses multithreading for some algorithms. This article presents how to exploit this.
Hello, this is my personal page where I save different resources and materials I find interesting, feel free to use any of the below! I am open towards feedback of any kind, so if you have any suggestions do not hesitate to contact me on any channel specified on my GitHub account!
Numpy uses BLAS, a linear algebra library which actually uses multithreading for some algorithms. This article presents how to exploit this.
This article which TimDbg wrote presents some curiosities he discovered while writing an x86 emulator.
This article explains modern microprocessor design, presenting many details which some may find interesting.
This article presents some of the more popular dispatch techniques used in the context of emulating. It has been of particular usefulness for me while writing an RISC-V executor.
This article presents a way to access the Google Chrome saved passwords on macOS, by exploiting some OS features.
This article critics some of the more popular programming languages in an ironic yet witty way.
This paper showcases some techniques that increases radix sort's versatility.
This paper is a direct continuation of the first one that I included, but by another author, that continues by presenting some optimizations brought to Pierre's implementation.
This is a video of a MIT class where SVMs are presented and explained.
This article explains the mathematics that stand behind SVMs.
This is a SO thread that contains some really helpful advice for implementing the softmax derivative.
This is a SO thread that explains how to properly use softmax in neural networks. It helped me understand how to propagate the derivative.
This article is a combination of a softmax implementation and an explanation. I decided to also include this one because it mentions a few tricks to keep the implementation numerically stable. It also showcases how easily it integrates with the cross entropy loss.
This article presents why and where softmax is useful.
This article presents a collection of information about CNNs from Stanford.
This article presents some fascinating techniques for reducing the parameter count in CNNs.
This article is a useful tutorial on transfer learning. I decided to include it in the CNNs category because it exemplifies transfer learning on them.
This article explains tiling and swizzling of textures in graphics.
Blog of a friend who posts mainly graphics programming related articles.