Awesome GitHub RepositoriesDimensionality Projection Plots

Visdom is a tool for scientific experiment tracking and real-time data monitoring. It provides a programmatic interface for broadcasting live plots, rich media, and training metrics from scripts to an interactive web dashboard. The project specializes in high-dimensional data analysis, offering capabilities to project complex feature sets into 2D space using t-SNE and visualize PyTorch model embeddings. It organizes visualizations into named environments, allowing users to isolate different experimental runs and compare plots across these environments in a single view. The system covers a br

This project is a collection of PyTorch learning resources and educational guides designed to teach the construction and training of neural networks. It serves as a comprehensive deep learning tutorial covering various model architectures and practical implementation strategies. The resources provide specific guidance on implementing computer vision tasks, such as image classification and synthetic imagery generation, as well as reinforcement learning agents using value networks and experience replay. It also covers sequential data modeling through recurrent networks and generative modeling u

This project is a manifold learning and non-linear dimensionality reduction library used to project high-dimensional data into lower-dimensional spaces while preserving topological structure. It functions as a parametric embedding framework and a topological data visualization library for identifying clusters and patterns within complex datasets. The library distinguishes itself through parametric neural mapping, which uses neural networks to learn functional mappings that allow for out-of-sample projections and the reconstruction of original data. It supports supervised and semi-supervised d

NonEuclidean is a graphics framework and rendering engine designed to compute and display three-dimensional scenes using non-standard spatial rules and geometries. It serves as a visualization tool for exploring complex mathematical spaces where traditional Euclidean laws of distance and angle do not apply. The project implements custom rendering pipelines to visualize non-standard geometric projections and warped spatial logic. This includes the ability to map non-Euclidean coordinates to Poincaré disk or Klein models to render curved space on flat screens. The system utilizes dynamic metri

Este proyecto es una librería de machine learning que proporciona una colección de implementaciones para algoritmos de aprendizaje supervisado y no supervisado. Sirve como un framework de deep learning, una colección de clasificadores estadísticos y una suite de herramientas para aprendizaje no supervisado y reducción de dimensionalidad. La librería permite la construcción de redes neuronales, incluyendo perceptrones multicapa y redes convolucionales para el reconocimiento de patrones. También proporciona herramientas para realizar análisis de componentes principales y aprendizaje de variedades (manifold learning) para visualizar datasets de alta dimensión, junto con una suite de algoritmos de clustering que agrupan datos no etiquetados mediante particionamiento iterativo. El proyecto cubre una amplia gama de capacidades de modelado predictivo, incluyendo tareas de clasificación y regresión usando árboles de decisión, k-vecinos más cercanos, clasificadores de Bayes, máquinas de vectores de soporte y regresión ridge. También incluye herramientas para flujos de trabajo de clasificación de imágenes y el análisis de datos no etiquetados.

Orange3 is a visual data mining platform that provides an interactive canvas for building data analysis workflows without writing code. At its core, it offers a widget-based visual programming environment where users connect configurable components to perform data preprocessing, machine learning model training, statistical evaluation, and interactive visualization. The platform is built on NumPy-backed data tables with domain descriptors that define variable names, types, and roles, and includes a lazy SQL query proxy for working with database tables without loading all data into memory. The

This is an interactive notebook-based course that teaches machine learning from Python fundamentals through deep learning and natural language processing. It uses real datasets and multiple frameworks within a structured, hands-on curriculum that combines concise explanations with executable code cells, built-in datasets, and embedded exercise checkpoints. Learning progresses through data preparation and exploration, classical machine learning workflows, computer vision with convolutional neural networks, and natural language processing with deep learning, all delivered as a cohesive progressi

Linfa es un framework de aprendizaje automático clásico y suite de aprendizaje estadístico implementado en Rust. Proporciona una colección de algoritmos para aprendizaje supervisado y no supervisado, centrados en métodos estadísticos tradicionales como regresión, clustering y árboles de decisión. El kit de herramientas se distingue por su capacidad de ser compilado en WebAssembly, permitiendo que los modelos analíticos se ejecuten dentro de entornos de navegador. Emplea una interfaz de algoritmo basada en traits para estandarizar el proceso de entrenamiento y predicción en sus diversos modelos. La biblioteca cubre una amplia gama de capacidades, incluyendo clasificación supervisada y regresión de valores continuos. Proporciona clustering no supervisado, métodos de conjunto (ensemble) para la agregación de modelos y procesamiento de señales mediante análisis de componentes independientes. La suite también incluye herramientas extensas de preprocesamiento de datos para normalización de características, vectorización de texto y reducción de dimensionalidad mediante PCA y t-SNE. Se proporcionan utilidades adicionales para la gestión de datos, incluyendo importación CSV y generación de conjuntos de datos sintéticos, así como herramientas de evaluación de modelos como matrices de confusión y métricas de validación cruzada.

Lit is a machine learning interpretability framework and model debugging tool designed to analyze model behavior and performance. It serves as an interpretability dashboard for large language models and a general performance analyzer for text, image, and tabular datasets. The project distinguishes itself through a comprehensive suite of interpretability tools, including salience map generation for feature attribution, the creation of synthetic and counterfactual examples to test robustness, and the projection of high-dimensional embeddings into visual spaces via UMAP or PCA. It further enable

Scanpy is a Python library for the preprocessing, visualization, and analysis of large-scale single-cell gene expression datasets. It serves as a toolkit for single-cell RNA sequencing analysis, providing a framework to process and analyze genomic data from individual cells to identify biological markers and cell types. The library includes a scalable data processing pipeline for cleaning and preparing genomic data, a clustering framework for grouping cells with similar expression profiles, and a system for modeling transitions between cell states to reconstruct biological development and dif

TensorFlow Similarity is a Python framework designed for training neural networks to learn high-dimensional vector representations and perform similarity-based retrieval. It provides a comprehensive toolkit for metric learning, enabling the development of systems that group similar items together in vector space and identify them through distance-based comparisons. The library distinguishes itself by integrating specialized training techniques, such as contrastive and triplet-based learning, with robust data management tools that ensure stable model convergence. It supports self-supervised re