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machine-learning

Готовая база из 5 881 ответа по 21 тесту hh.ru - сдать с первой попытки https://it-mountain.ru/hh-tests

Official repository of the Ruhr university Neural Network energy representation (RuNNer).

Signed, checksum-pinned, provenance-tracked ONNX models for krites (spec 0031).

RuNNerASE is a collection of packages for training, evaluating, and analyzing machine learning potentials with RuNNer, the Ruhr university Neural Network energy representation.

BGone: A GUI-based Python utility engineered to remove image backgrounds using a suite of AI models. It streamlines testing by processing one image with multiple algorithms (rembg, mediapipe) and saving separate, transparent outputs for easy side-by-side comparison.

Self-learning diagnostics agent for GNU/Linux that uses unsupervised machine learning to detect anomalies in system logs in real-time.

Full stack IoT predictive maintenance platform with embedded firmware, sensor telemetry, FastAPI services, PostgreSQL storage, dashboards and anomaly detection.

pt_kmeans is a high-performance, pure PyTorch K-Means implementation for CPU/GPU, featuring K-Means++ initialization, hierarchical clustering, and cluster splitting, optimized for large-scale datasets.

MLX port of JoyAI-Video-Edit — 16B streaming instruction-guided video editing (DiT + causal VAE + MiMo-VL) native on Apple Silicon

Master thesis: free and open-source hands-free cursor control through gaze and facial gestures, for people with severe motor paralysis.

ML-driven threat detection and continuous monitoring platform built for federal zero trust architectures.

Machine-learning models in the browser with TensorFlow.js and Bun.

A small, transparent experiment testing whether language models distinguish solvable prompts from prompts containing missing or contradictory information, and whether their stated confidence tracks correctness

Finally a smart RSS reader which doesn't suck ass or your data.

CasperEngine - Sovereign AI inference engine with NIYAH kernel. ARM64 native C implementation with custom tokenizer, GGUF model support, and pure mathematical AI training pipeline.

Source code for ai-folio.gitlab.io, a personal blog and portfolio exploring AI news, tools, and industry trends.

Ce projet porte sur le développement d'un modèle de scoring bancaire destiné à prédire le risque associé à une demande de crédit à partir des données historiques de prêts du dataset Lending Club.

L'objectif est de construire une chaîne complète de Data Science, allant de l'analyse et de la préparation des données jusqu'à la modélisation prédictive et l'évaluation des performances.

Travaux réalisés :

Analyse exploratoire du dataset et étude des différentes variables disponibles. Nettoyage et préparation des données. Traitement des valeurs manquantes et des variables catégorielles. Sélection et transformation des variables pertinentes. Réalisation de Feature Engineering afin de construire des variables adaptées à la prédiction. Préparation des jeux de données pour l'entraînement et l'évaluation. Expérimentation de différents modèles de Machine Learning. Expérimentation d'un modèle de Deep Learning avec TensorFlow Évaluation des modèles à l'aide de métriques de classification. Analyse comparative des performances afin d'identifier l'approche la plus pertinente

The AI and the Automobile collection brings together multiple specifications that define how AI integrates into modern vehicle systems, treating the automobile as a software-defined platform where intelligence spans perception, control, navigation, and energy management. It emphasizes real-time performance, safety-critical architecture, and the transition from purely mechanical systems to continuously evolving software-driven mobility. Across the collection, the specs also describe how automotive AI should be built through open and interoperable systems with strong safety practices, including simulation, validation pipelines, and human-in-the-loop oversight to handle edge cases. Together, they frame vehicle intelligence as a layered system combining autonomy, redundancy, diagnostics, and secure update mechanisms to enable reliable and scalable deployment. https://roxanneardary.com/ai-and-the-automobile/

Aston University DG1AID lab repository with AI and data science notes, Python notebooks, NumPy, Pandas, search algorithms and machine learning practice.

A production-grade machine learning system for classifying celestial objects — stars, galaxies, and quasars — from 500,000 photometric observations sourced from the Sloan Digital Sky Survey (SDSS DR18). The pipeline covers LOF-based outlier detection, SMOTEENN class balancing, and SelectKBest feature selection, with six classifier configurations benchmarked against each other. Random Forest achieved the highest accuracy at ~99.51%, while LightGBM was selected for deployment due to its faster inference, smaller footprint, and clean ONNX export path. The system is fully containerized with Docker, backed by a CI/CD pipeline, and served live via a Gradio interface on Hugging Face Spaces.