:fireworks:Interactive Online Platform that Visualizes Algorithms from Code
- algorithm
- data-structure
- visualization
- animation
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:fireworks:Interactive Online Platform that Visualizes Algorithms from Code
Turns Data and AI algorithms into production-ready web applications in no time.
Visualize call graph of a Go program using Graphviz
Uplift modeling and causal inference with machine learning algorithms
Scalable and user friendly neural :brain: forecasting algorithms.
Create word clouds in JavaScript.
Graph drawing library for JavaScript
Math.NET Numerics
Up to 100x faster strings for C, C++, CUDA, Python, Rust, Swift, JS, & Go, leveraging NEON, AVX2, AVX-512, SVE, GPGPU, & SWAR to accelerate search, hashing, sorting, edit distances, sketches, and memory ops 🦖
The machine learning toolkit for time series analysis in Python
A comprehensive set of fairness metrics for datasets and machine learning models, explanations for these metrics, and algorithms to mitigate bias in datasets and models.
Algorithms for outlier, adversarial and drift detection
Interactive Go programming with Jupyter
Powerful modern math library for PHP: Features descriptive statistics and regressions; Continuous and discrete probability distributions; Linear algebra with matrices and vectors, Numerical analysis; special mathematical functions; Algebra
Automatically Visualize any dataset, any size with a single line of code. Created by Ram Seshadri. Collaborators Welcome. Permission Granted upon Request.
Layout algorithms for visualizing directed acyclic graphs
Owl - OCaml Scientific Computing @ https://ocaml.xyz
ObsPy: A Python Toolbox for seismology/seismological observatories.
GraphFrames is a package for Apache Spark which provides DataFrame-based Graphs
A software library of stochastic streaming algorithms, a.k.a. sketches.
Algorithm is a library of tools that is used to create intelligent applications.
Use evolutionary algorithms instead of gridsearch in scikit-learn
Environment for reinforcement-learning algorithmic trading models
A light-weight deep reinforcement learning framework for portfolio management. This project explores the possibility of applying deep reinforcement learning algorithms to stock trading in a highly modular and scalable framework.