Displaying 1 to 9 from 9 results

from-python-to-numpy - An open-access book on numpy vectorization techniques, Nicolas P

  •    HTML

There are already a fair number of books about NumPy (see bibliography) and a legitimate question is to wonder if another book is really necessary. As you may have guessed by reading these lines, my personal answer is yes, mostly because I think there is room for a different approach concentrating on the migration from Python to NumPy through vectorization. There are a lot of techniques that you don't find in books and such techniques are mostly learned through experience. The goal of this book is to explain some of these techniques and to provide an opportunity for making this experience in the process.

text2vec - Fast vectorization, topic modeling, distances and GloVe word embeddings in R.

  •    R

text2vec is an R package which provides an efficient framework with a concise API for text analysis and natural language processing (NLP). To learn how to use this package, see text2vec.org and the package vignettes. See also the text2vec articles on my blog.

neanderthal - Fast Clojure Matrix Library

  •    Clojure

Neanderthal is a Clojure library for fast matrix and linear algebra computations based on the highly optimized native libraries of BLAS and LAPACK computation routines for both CPU and GPU.. Read the documentation at Neanderthal Web Site.

Vc - SIMD Vector Classes for C++

  •    C++

Recent generations of CPUs, and GPUs in particular, require data-parallel codes for full efficiency. Data parallelism requires that the same sequence of operations is applied to different input data. CPUs and GPUs can thus reduce the necessary hardware for instruction decoding and scheduling in favor of more arithmetic and logic units, which execute the same instructions synchronously. On CPU architectures this is implemented via SIMD registers and instructions. A single SIMD register can store N values and a single SIMD instruction can execute N operations on those values. On GPU architectures N threads run in perfect sync, fed by a single instruction decoder/scheduler. Each thread has local memory and a given index to calculate the offsets in memory for loads and stores. Current C++ compilers can do automatic transformation of scalar codes to SIMD instructions (auto-vectorization). However, the compiler must reconstruct an intrinsic property of the algorithm that was lost when the developer wrote a purely scalar implementation in C++. Consequently, C++ compilers cannot vectorize any given code to its most efficient data-parallel variant. Especially larger data-parallel loops, spanning over multiple functions or even translation units, will often not be transformed into efficient SIMD code.




xsimd - Modern, portable C++ wrappers for SIMD intrinsics and parallelized, optimized math implementations

  •    C++

SIMD (Single Instruction, Multiple Data) is a feature of microprocessors that has been available for many years. SIMD instructions perform a single operation on a batch of values at once, and thus provide a way to significantly accelerate code execution. However, these instructions differ between microprocessor vendors and compilers. xsimd provides a unified means for using these features for library authors. Namely, it enables manipulation of batches of numbers with the same arithmetic operators as for single values. It also provides accelerated implementation of common mathematical functions operating on batches.

node-autotrace - A simple interface for converting raster images into vector graphics using AutoTrace

  •    Javascript

A simple interface for converting raster images into vector graphics using AutoTrace. More details about AutoTrace can be found here.

gotrace - Pure Go implementation of Potrace vectorization library

  •    C

Pure Go implementation of Potrace vectorization library. Supports simple SVG output generation. Also includes cgo bindings for the original Potrace library.

multivariate-linear-regression-gradient-descent-javascript - ⭐️ Multivariate Linear Regression with Gradient Descent in JavaScript (Vectorized)

  •    Javascript

This example project demonstrates how the gradient descent algorithm may be used to solve a multivariate linear regression problem. Read more about it here.