Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
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Pytorch Seq2seq | 5,024 | 3 months ago | mit | Jupyter Notebook | ||||||
Tutorials on implementing a few sequence-to-sequence (seq2seq) models with PyTorch and TorchText. | ||||||||||
Nlp Models Tensorflow | 1,329 | 4 years ago | 3 | mit | Jupyter Notebook | |||||
Gathers machine learning and Tensorflow deep learning models for NLP problems, 1.13 < Tensorflow < 2.0 | ||||||||||
Eeg Dl | 563 | a year ago | 10 | May 13, 2020 | 4 | mit | Python | |||
A Deep Learning library for EEG Tasks (Signals) Classification, based on TensorFlow. | ||||||||||
Seq2seq | 502 | 4 years ago | 11 | mit | Python | |||||
Minimal Seq2Seq model with Attention for Neural Machine Translation in PyTorch | ||||||||||
Sohu_competition | 216 | 6 years ago | Jupyter Notebook | |||||||
Sohu's 2018 content recognition competition 1st solution(搜狐内容识别大赛第一名解决方案) | ||||||||||
Neural Summ Cnndm Pytorch | 194 | 2 years ago | 4 | mit | Python | |||||
Neural abstractive summarization (seq2seq + copy (or pointer network) + coverage) in pytorch on CNN/Daily Mail | ||||||||||
Train_arch | 154 | 3 years ago | Python | |||||||
cnn+rnn+attention: vgg(vgg16,vgg19)+rnn(LSTM, GRU)+attention, resnet(resnet_v2_50,resnet_v2_101,resnet_v2_152)+rnnrnn(LSTM, GRU)+attention, inception_v4+rnn(LSTM, GRU)+attention, inception_resnet_v2+rnn(LSTM, GRU)+attention,..... vgg(vgg16,vgg19), resnet(resnet_v2_50,resnet_v2_101,resnet_v2_152), inception_v4, inception_resnet_v2,..... | ||||||||||
Prediction Flow | 136 | 3 years ago | 5 | March 11, 2021 | 1 | mit | Python | |||
Deep-Learning based CTR models implemented by PyTorch | ||||||||||
Nmtpy | 128 | 6 years ago | other | Python | ||||||
nmtpy is a Python framework based on dl4mt-tutorial to experiment with Neural Machine Translation pipelines. | ||||||||||
Deep Summarization | 126 | 6 years ago | 5 | mit | Python | |||||
Uses Recurrent Neural Network (LSTM/GRU/basic_RNN units) for summarization of amazon reviews |