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.

Deterministic domain encoders with embedding-space arithmetic (IEEE-754 semantics)

⚡ Auto-JIT your Python code with Numba @njit + optional AOT compilation 🚀 Numeric-heavy code, boosted fast 🏎️

The code provides a collection of functions for matrix changes, including transposing, finding the rank, inverting, multiplying.

Memory-safe clean-room Rust reimplementation of NumPy with strict/hardened compatibility modes, deterministic stride calculus, differential conformance harnesses, and RaptorQ durability artifacts.

A complete hands-on NumPy learning repository covering array creation, indexing, slicing, reshaping, broadcasting, mathematical operations, boolean indexing, aggregation functions, and real-world use cases.

Arbitrary Numbers

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.

Explore Python implementations of numerical methods for solving mathematical problems. Includes root-finding, integration, and more.

NumPy learning repository with step-by-step practice, array operations, indexing, slicing, and probability distributions for data science.

Interactive statistics calculator built with Python, NumPy, and Tkinter that allows users to input numbers and compute statistical values such as sum, maximum, mean, average, and standard deviation through a graphical interface.

Python package for calculating weighted Matthews Correlation Coefficient (MCC) and its multiclass extensions (ECC, MPC1, MPC2) with theoretical robustness guarantees.

Transpiler converting IBM APL2 code and workspaces to Python/NumPy

Exercises for the University of Bologna Numerical Computing course (a.y. 2021-22).

This repository demonstrates a hybrid Python/C++ architecture for performance-critical scientific workloads

Project for the University of Bologna Numerical Computing course (a.y. 2021-22).

⚡ Accelerate Python performance with Autonumba by auto-injecting JIT compilation, enhancing speed for files and libraries effortlessly.

Implementation of several numerical computing methods with Python /// by theMHD.

🛠 Reimplement NumPy in Rust for full compatibility, memory safety, and better performance with a clear, reliable architecture.

TensorFlow with rhythm — simple, flexible neural networks without the Keras overhead.