txxhoney,github

benchmarking-gnns

Repository for benchmarking graph neural networks

csradar

Compressed Sensing for improved radar target classification

deep-unet-for-satellite-image-segmentation

Satellite Imagery Feature Detection with SpaceNet dataset using deep UNet

deep_learning_lstm

Building a classifier model for the Multivariate Time Series data using LSTM in Keras

deeplearningcoursecodes

Notes, Codes, and Tutorials for the Deep Learning Course at ChinaHadoop

Matlab/Octave toolbox for deep learning. Includes Deep Belief Nets, Stacked Autoencoders, Convolutional Neural Nets, Convolutional Autoencoders and vanilla Neural Nets. Each method has examples to get you started.

dl4sci-pytorch-webinar

evolutionary-algorithm

Evolutionary Algorithm using Python

fast-fuzzy-c-means-segmentation

Segment N-dimensional grayscale images into c classes using efficient c-means or fuzzy c-means clustering algorithm

fcn-for-semantic-segmentation

Implemention of FCN-8 and FCN-16 with Keras and uses CRF as post processing

fcn.berkeleyvision.org

Fully Convolutional Networks for Semantic Segmentation by Jonathan Long*, Evan Shelhamer*, and Trevor Darrell. CVPR 2015 and PAMI 2016.

framework

Machine learning, computer vision, statistics and general scientific computing for .NET

from_ft_to_wt_examples-

fuzzy-c-means-clustering-sonar-image-segmentation

An Implementation of Sonar Image Segmentation through Fuzzy C-means Clustering

gnnpapers

Must-read papers on graph neural networks (GNN)

gpt_academic

为GPT/GLM等LLM大语言模型提供实用化交互接口，特别优化论文阅读/润色/写作体验，模块化设计，支持自定义快捷按钮&函数插件，支持Python和C++等项目剖析&自译解功能，PDF/LaTex论文翻译&总结功能，支持并行问询多种LLM模型，支持chatglm3等本地模型。接入通义千问, deepseekcoder, 讯飞星火, 文心一言, llama2, rwkv, claude2, moss等。

graphneuralnetwork

《深入浅出图神经网络：GNN原理解析》配套代码

handson-ml2

A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.

iic

Invariant Information Clustering for Unsupervised Image Classification and Segmentation

image-segmentation-using-fast-fuzzy-c-means-clusering

A fast and robust fuzzy c-means clustering algorithms, namely FRFCM, is proposed. The FRFCM is able to segment grayscale and color images and provides excellent segmentation results.