머신러닝 · 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.

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