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

This project is a machine learning library providing a collection of implementations for supervised and unsupervised learning algorithms. It serves as a deep learning framework, a statistical classifier collection, and a suite of tools for unsupervised learning and dimensionality reduction. The library enables the construction of neural networks, including multi-layer perceptrons and convolutional networks for pattern recognition. It also provides tools for performing principal component analysis and manifold learning to visualize high-dimensional datasets, alongside a suite of clustering alg

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 este un framework de machine learning clasic și o suită de învățare statistică implementată în Rust. Oferă o colecție de algoritmi pentru învățare supervizată și nesupervizată, axată pe metode statistice tradiționale precum regresia, clustering-ul și arborii de decizie. Toolkit-ul se distinge prin capacitatea de a fi compilat în WebAssembly, permițând modelelor analitice să ruleze în medii de browser. Utilizează o interfață de algoritm bazată pe trăsături (traits) pentru a standardiza procesul de antrenare și predicție în diferitele sale modele. Biblioteca acoperă o gamă largă de capabilități, inclusiv clasificarea supervizată și regresia valorilor continue. Oferă clustering nesupervizat, metode de ansamblu pentru agregarea modelelor și procesarea semnalelor prin analiza componentelor independente. Suita include, de asemenea, instrumente extinse de preprocesare a datelor pentru normalizarea caracteristicilor, vectorizarea textului și reducerea dimensionalității folosind PCA și t-SNE. Utilitare suplimentare sunt furnizate pentru gestionarea datelor, inclusiv importul CSV și generarea de seturi de date sintetice, precum și instrumente de evaluare a modelelor, cum ar fi matricile de confuzie și metricile de validare încrucișată.

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