機械学習 · GitHub

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)

Speedy Wavenet generation using dynamic programming :zap:

Pytorch framework for doing deep learning on point clouds.

TorchDR - PyTorch Dimensionality Reduction

• High Performance - Engineered for speed with GPU acceleration, torch.compile support, and optimized algorithms leveraging sparsity and negative sampling.
• Multi-GPU Support - Scale to massive datasets with built-in distributed computing. Use the torchdr CLI or torchrun for easy multi-GPU execution of compatible modules and methods.

A Python toolkit for fine-tuning Geospatial Foundation Models (GFMs).

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/

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.

誰でも使える統計・アルゴリズム投資戦略

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