numerical-computing
There are 53 repositories under numerical-computing topic.
stdlib-js/stdlib
✨ The fundamental numerical library for JavaScript and TypeScript. ✨
PDLPorters/pdl
Scientific computing with Perl
farukalpay/FABE
High-accuracy SIMD sin/cos/sincos library in C with AVX2, AVX-512, and NEON support. Full-range reduction. Fast at scale. Portable by design.
SciScala/NDScala
N-dimensional / multi-dimensional arrays (tensors) in Scala 3. Think NumPy ndarray / PyTorch Tensor but type-safe over shapes, array/axis labels & numeric data types
ishmahafeez/Computer-Science-Notes
Computer Science PDF notes
rohanmistry231/Numpy-Interview-Preparation
A focused resource for mastering NumPy, featuring practice problems, code examples, and interview-oriented numerical computing techniques in Python. Covers array operations, linear algebra, and performance optimization for data science interviews.
chenmingxiang110/J4darrays
A java package for nd-array calculations
shahzaibk23/Numerical-Computing-Python
Python implementation of Methods and Algorithm or Numerical Computing Course. You just have to Enter the input values from the question and All the iterations will be generated automatically.
muditbhargava66/FFT-implementation-in-C
The repository provides code, build instructions, and usage guidelines for each FFT implementation.
dusky04/goopy
a basic numpy-like library in c with broadcasting :)
AayushKotwani3/Numpy-masterclass
Comprehensive hands-on notes, examples, and exercises to master NumPy from basics to advanced.
gramian/matrico
A flonum matrix module for CHICKEN Scheme.
HolubievIllya/Matrix-Operations-Python
The code provides a collection of functions for matrix changes, including transposing, finding the rank, inverting, multiplying.
shafaq-aslam/numpy-lab
A structured collection of Jupyter notebooks exploring NumPy from the ground up; covering array creation, manipulation, broadcasting, indexing, and data visualization for scientific computing and data analysis.
arbitrary-number/arbitrary-number
Arbitrary Numbers
labex-labs/numpy-practice-challenges
This course contains lots of challenges for NumPy, each challenge is a small NumPy project with detailed instructions and solutions. You can practice your NumPy skills by solving these challenges, improve your problem-solving skills, and learn how to write clean and efficient code.
muhammad-umaair/NumericalAnalysis
Complete numerical computing labs with pdf documents. You just need to change coding according to your question requirements.
NechbaMohammed/OptiNumPy
This repository contains the source code and documentation for the OptiNumPy library, a numerical analysis optimization package written in Python. The library provides various numerical optimization algorithms for solving optimization problems.
Sourabh-Kumar04/Numpy-Basic
Numpy-Basic is a structured learning repo covering NumPy from basics to advanced. It includes arrays, indexing, reshaping, filtering, vector ops, angle functions, stats, and .npy file handling. Each concept is explained with code, examples, and Matplotlib visualizations in both light and dark modes. Ideal for students and data learners.
Abdirashid-dv/Numerical-Methods-Using-Python
Explore Python implementations of numerical methods for solving mathematical problems. Includes root-finding, integration, and more.
Abhrankan-Chakrabarti/sqrt
A high-precision square root calculator written in Rust using the Malachite library. Supports both interactive and command-line modes, allowing users to compute the square root of any positive integer to an arbitrary number of decimal digits with fixed-point accuracy.
AitzazTahirCh/Numerical-Computing
This repository contains Numerical Computing important concepts notes (leading to machine learning and approximately in every field of computer science) implemented in Python Notebooks.
labex-labs/100-numpy-exercises
NumPy is an extension library for Python language, supporting operations of a large number of high-dimensional arrays and matrices. In addition, it also provides a large number of mathematical function libraries for array operations. Machine learning involves a lot of transformations and operations on arrays, which makes NumPy one of the essenti...
NeuralAditya/Polynomial_Regression_C
A high-performance polynomial regression implementation in pure C with gradient descent optimization and visualization support.
abbaskhan0345/numpy-learning
NumPy Essentials in Python – A collection of examples and mini-projects demonstrating array manipulation, mathematical operations, broadcasting, and performance optimization using NumPy. This repo showcases key concepts for scientific computing and data analysis workflows.
AS-0167/zero-hunter
Zero Hunter - A comprehensive C-based root-finding toolkit implementing five numerical methods with modular design, multiple stopping criteria, and extensive testing capabilities for solving nonlinear equations.
Ayikoandrew/odin-ML
Exploring Machine Learning at a low level. Implementing algorithms from scratch in Odin, one Sunday at a time.
harinandanmv/numpy-practice
A collection of beginner-friendly NumPy tasks designed to explore fundamental array operations in Python. Covers array creation, reshaping, slicing, mathematical operations, identity matrices, random data generation, and more — perfect for hands-on practice and foundational learning in scientific computing with NumPy.
kandarpa02/hiphop
TensorFlow with rhythm — simple, flexible neural networks without the Keras overhead.
labex-labs/numpy-for-beginners
This comprehensive course covers the fundamental concepts and practical techniques of NumPy, the essential library for numerical computing in Python. Learn to create, manipulate, and analyze arrays efficiently.
Mahsa-Goudarzi/scientific-computing-tools-course
The exercises for the course "Scientific computing tools" - Autumn 2024
nabilshadman/learning-jax-numerical-computing-machine-learning
Exercise files of the Learning JAX course (on LinkedIn Learning)
rtgrt5645/numpy-lab
🧮 Explore, manipulate, and visualize data with NumPy to enhance your Python skills in scientific computing and data analysis.
SatvikPraveen/OctaveMasterPro
Advanced numerical computing framework with GNU Octave featuring parallel processing, optimization algorithms, statistical analysis, and comprehensive data science applications. Includes Dockerized Jupyter environment, financial modeling, signal/image processing pipelines, industrial analytics, and real-world demonstrations.
Sidra-009/Numpy-Practice
Practice codes and examples of NumPy basics including array creation, indexing, slicing, broadcasting, random functions, and visualization with Matplotlib.