: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.
Open Machine Learning Course
🦙 LaMa Image Inpainting, Resolution-robust Large Mask Inpainting with Fourier Convolutions, WACV 2022
Statistical Machine Intelligence & Learning Engine
Simple and Distributed Machine Learning Python Library porting ML algorithms for Spark
Performance analysis of predictive (alpha) stock factors
Scalable and user friendly neural :brain: forecasting algorithms.
C++ implementation of the Python Numpy library
Create word clouds in JavaScript.
Graph drawing library for JavaScript
KTH Algorithm Competition Template Library (... eller KTHs AC-tillverkande lapp)
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
StellarGraph - Machine Learning on Graphs
Quantitative analysis, strategies and backtests
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
Providing the solutions for high-frequency trading (HFT) strategies using data science approaches (Machine Learning) on Full Orderbook Tick Data.
Source code of PyGAD, a Python 3 library for building the genetic algorithm and training machine learning algorithms (Keras & PyTorch).
Automatically Visualize any dataset, any size with a single line of code. Created by Ram Seshadri. Collaborators Welcome. Permission Granted upon Request.
A minimal benchmark for scalability, speed and accuracy of commonly used open source implementations (R packages, Python scikit-learn, H2O, xgboost, Spark MLlib etc.) of the top machine learning algorithms for binary classification (random forests, gradient boosted trees, deep neural networks etc.).
Layout algorithms for visualizing directed acyclic graphs
A curated list of awesome links and software libraries that are useful for robots.