Popular GitHub Repositories for Classify

Natural language detection

中文长文本分类、短句子分类、多标签分类、两句子相似度（Chinese Text Classification of Keras NLP, multi-label classify, or sentence classify, long or short），字词句向量嵌入层（embeddings）和网络层（graph）构建基类，FastText，TextCNN，CharCNN，TextRNN, RCNN, DCNN, DPCNN, VDCNN, CRNN, Bert, Xlnet, Albert, Attention, DeepMoji, HAN, 胶囊网络-CapsuleNet, Transformer-encode, Seq2seq, SWEM, LEAM, TextGCN

SCAN: Learning to Classify Images without Labels, incl. SimCLR. [ECCV 2020]

? ? Use ML to classify flows and packets as benign or malicious. ? ?

C++ implementation to Detect, track and classify multiple objects using LIDAR scans or point cloud

based on yolo-high-level project (detect\pose\classify\segment\):include yolov5\yolov7\yolov8\ core ,improvement research ,SwintransformV2 and Attention Series. training skills, business customization, engineering deployment C

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Classify camera images locally using TensorFlow models

Classify Kaggle San Francisco Crime Description into 39 classes. Build the model with CNN, RNN (GRU and LSTM) and Word Embeddings on Tensorflow.

Classify music genre from a 10 second sound stream using a Neural Network.

Implementation of Single Shot MultiBox Detector in TensorFlow, to detect and classify traffic signs

Implementation of CNN on ZYNQ FPGA to classify handwritten numbers using MNIST database

Classify Kaggle Consumer Finance Complaints into 11 classes. Build the model with CNN (Convolutional Neural Network) and Word Embeddings on Tensorflow.

A python program to detect and classify hand pose using deep learning techniques

TextClf ：基于Pytorch/Sklearn的文本分类框架，包括逻辑回归、SVM、TextCNN、TextRNN、TextRCNN、DRNN、DPCNN、Bert等多种模型，通过简单配置即可完成数据处理、模型训练、测试等过程。

This case study shows how to create a model for text analysis and classification and deploy it as a web service in Azure cloud in order to automatically classify support tickets. This project is a proof of concept made by Microsoft (Commercial Software Engineering team) in collaboration with Endava http://endava.com/en

A new approach based on a 10-layer one-dimensional convolution neural network (1D-CNN) to classify five brain states (four MI classes plus a 'baseline' class) using a data augmentation algorithm and a limited number of EEG channels. Paper: https://doi.org/10.1088/1741-2552/ac4430

Using spiking neurons and spike-timing-dependent plasticity to classify the MNIST handwritten digits.

Face model to classify gender and race. Trained on LFWA+ Dataset.

Project is about designing a Trained Neural Network on FPGA to classify an Image Input using CNN.