Awesome GitHub RepositoriesDensity Estimation

This project is an interactive data science environment that combines code execution, rich media visualization, and narrative documentation into a persistent, browser-based platform. It serves as a comprehensive educational resource for scientific computing, providing a framework for iterative data analysis and machine learning prototyping. The environment is distinguished by its focus on high-performance numerical computing, utilizing vectorized array operations and memory-mapped data structures to handle large-scale computations efficiently. It features a unified estimator interface that st

Gaussian Splatting is a computational framework designed to transform sparse sets of two-dimensional photographs into photorealistic, interactive three-dimensional scene representations. The system functions as a reconstruction tool and rendering engine, enabling the conversion of image data into volumetric models that support novel view synthesis. The project represents scenes as a collection of anisotropic three-dimensional Gaussians, which store position, opacity, color, and covariance data. It distinguishes itself through a differentiable tile-based rasterization process that projects the

This project is a static educational website and comprehensive curriculum focused on computer vision and deep learning. It serves as a public repository of instructional materials, lecture notes, and technical guides specifically detailing convolutional neural networks and visual recognition. The site is developed using static-site generation to host course documentation and student project directories. It provides structured academic resources that guide learners through image classification, generative modeling, and the implementation of various neural network architectures. The curriculum

This project is a collection of educational resources and implementation frameworks providing deep learning model recipes, code samples, and step-by-step guides for computer vision tasks. It organizes complex workflows into modular recipes and implementation guides to facilitate the building of image and video analysis models. The framework focuses on specialized vision capabilities, including an image similarity framework for fast retrieval and re-ranking, human pose estimation, and video action recognition. It also provides specific tools for crowd density estimation and document image clea

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

This repository provides a collection of reference implementations and code examples for training and deploying machine learning models using the MLX framework. It serves as a practical guide for executing distributed training, fine-tuning large language models, converting model weights, and implementing multimodal generative workflows. The project distinguishes itself through specialized examples for local hardware execution, featuring weight quantization to reduce memory usage and low-rank adaptation for parameter-efficient fine-tuning. It also includes scripts for transforming external mod

This project is a crowd density estimator and WiFi probe request monitor that calculates approximate person counts by analyzing MAC addresses and signal strength from nearby devices. It functions as a packet capture tool that detects smartphone WiFi signals to estimate the number of people in a surrounding area. The system includes a network presence visualizer that uses browser-based plotting to track device trajectories and occupancy trends over time. It also serves as a data capture utility that saves detected device manufacturer details and signal data into structured JSON files for furth

PRMLT provides self-contained MATLAB implementations of every algorithm from the Pattern Recognition and Machine Learning textbook by Christopher Bishop. The code reproduces the book's exact formulas and notation, making each implementation directly traceable to the source material for educational verification and study. The implementations cover the full range of core machine learning methods from the textbook, including classification, clustering, regression, density estimation, and neural network algorithms. Each module is self-contained with heavy comments, and the code uses compact, vect

Anomalib is a PyTorch-based library for visual anomaly detection, offering a modular framework, a comprehensive model zoo, and a benchmarking suite designed for industrial defect detection. It provides a wide range of algorithms—including generative, discriminative, teacher-student, and vision-language approaches—that support unsupervised, few-shot, and zero-shot settings. The library enables deployment through model export to ONNX and OpenVINO for edge devices, and includes a no-code web application for training and inference. It also features a command-line interface for orchestrating multi

Dieses Projekt ist eine Support-Vector-Machine-Bibliothek, die in C implementiert ist und eine Engine für Klassifizierungs- und Regressionsaufgaben bereitstellt. Sie fungiert als Machine-Learning-Kernel-Bibliothek und statistischer Modell-Validator, der verwendet wird, um Datenpunkte zu kategorisieren und kontinuierliche numerische Werte vorherzusagen. Die Bibliothek ermöglicht die Definition benutzerdefinierter Kernel-Funktionen, um die Ähnlichkeit zwischen Datenpunkten in spezialisierten Datensätzen zu berechnen. Sie enthält zudem Tools für probabilistische Modellierung, wie die Schätzung der Klassenzugehörigkeit, Datendichte und Verteilungsgrenzen. Breite Funktionen decken das Modelltraining für Multi-Class-Datensätze ab, einschließlich des Managements unausgewogener Daten durch gewichtete Loss-Funktionen. Das System bietet Workflows für die Hyperparameter-Auswahl und Modelloptimierung mittels Genauigkeitskonturen und stratifizierter Kreuzvalidierung. Daten-Preprocessing-Utilities für Input-Validierung und Attribut-Skalierung zur Normalisierung von Feature-Größen sind enthalten.