Maschinelles Lernen · GitHub

A PyTorch-based library for semi-supervised learning (NeurIPS'21)

This repository contains the source code for the semantic image segmentation method described in the ICCV 2015 paper: Conditional Random Fields as Recurrent Neural Networks. http://crfasrnn.torr.vision/

High-fidelity performance metrics for generative models in PyTorch

Advanced Transformer Summarization Suite: Abstract & Extractive AI Models 2026

Examples and libraries for "Natural Language Processing in Action" book

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.

Statistische und algorithmische Anlagestrategien für alle

Find big moving stocks before they move using machine learning and anomaly detection

Train a deep learning net with OpenStreetMap features and satellite imagery.

Semantic Segmentation on PyTorch (include FCN, PSPNet, Deeplabv3, Deeplabv3+, DANet, DenseASPP, BiSeNet, EncNet, DUNet, ICNet, ENet, OCNet, CCNet, PSANet, CGNet, ESPNet, LEDNet, DFANet)

A PyTorch Implementation of Fast-SCNN: Fast Semantic Segmentation Network

Keras-Modelle im Browser ausführen, mit GPU-Unterstützung über WebGL.

🤖 Interactive Machine Learning experiments: 🏋️models training + 🎨models demo

Official PyTorch implementation of DD3D: Is Pseudo-Lidar needed for Monocular 3D Object detection? (ICCV 2021), Dennis Park*, Rares Ambrus*, Vitor Guizilini, Jie Li, and Adrien Gaidon.

The Triton backend that allows running GPU-accelerated data pre-processing pipelines implemented in DALI's python API.