Maschinelles Lernen · GitHub

Toolkit for training and evaluating Self-Supervised Learning (SSL) frameworks for Speaker Verification (SV).

• Supervised and Self-supervised datasets (siamese and DINO sampling)
• Audio augmentation (noise and reverberation)

Ein Ressourcen-Repository für 3D-Machine-Learning

MCP server for Lacuna, a research map for machine learning: search directions, papers, authors, venues, and AI-generated research proposals.

Graph Convolutional Networks in PyTorch

real-time fire detection in video imagery using a convolutional neural network (deep learning) - from our ICIP 2018 paper (Dunnings / Breckon) + ICMLA 2019 paper (Samarth / Bhowmik / Breckon)

Reinforcement Learning for real-time applications

• Analog control from screenshots: The tmrl example pipeline trains policies that are able to drive from raw screenshots captured in real-time. For beginners, we also provide simpler rangefinder ("LIDAR") observations, which are less potent but easier to learn from. The example pipeline controls the game via a virtual gamepad, which enables analog actions.
• Models: To process LIDAR measurements, the example tmrl pipeline uses a Multi-Layer Perceptron (MLP). To process raw camera images (snapshots), it uses a Convolutional Neural Network (CNN). These models learn the physics of the game from histories or observations equally spaced in time.

[NeurIPS2026] Flexible scientific foundation models trained with synthetic online data generation

Готовая база из 5 881 ответа по 21 тесту hh.ru - сдать с первой попытки

Investment Funnel 📈 is an open-source python platform designed for an easy development and backtesting of outperforming investment strategies.

This is an official PyTorch implementation of our NeurIPS 2023 paper "GeoCLIP: Clip-Inspired Alignment between Locations and Images for Effective Worldwide Geo-localization"

Code, results and checkpoints for the paper "Silent Pipeline Defects in World-Model RL: A Dreamer 4 Case Study": a PyTorch reimplementation of Dreamer 4, two silent defects that passed 12 checks, and three cheap checks that catch them

A hands-on course on modern LLM architectures, training, and inference, with step-by-step PyTorch implementations and runnable notebooks.