| RubensZimbres/Repo-2017 |
1,146 |
|
0 |
0 |
over 4 years ago |
0 |
|
1 |
|
Python |
| Python codes in Machine Learning, NLP, Deep Learning and Reinforcement Learning with Keras and Theano |
| avisingh599/visual-qa |
476 |
|
0 |
0 |
almost 8 years ago |
0 |
|
24 |
mit |
Python |
| [Reimplementation Antol et al 2015] Keras-based LSTM/CNN models for Visual Question Answering |
| bradleypallen/keras-quora-question-pairs |
357 |
|
0 |
0 |
almost 9 years ago |
0 |
|
4 |
mit |
Jupyter Notebook |
| A Keras model that addresses the Quora Question Pairs dyadic prediction task. |
| oswaldoludwig/Seq2seq-Chatbot-for-Keras |
309 |
|
0 |
0 |
about 7 years ago |
0 |
|
10 |
apache-2.0 |
Python |
| This repository contains a new generative model of chatbot based on seq2seq modeling. |
| chen0040/keras-text-summarization |
243 |
|
0 |
0 |
over 7 years ago |
0 |
|
16 |
mit |
Python |
| Text summarization using seq2seq in Keras |
| minimaxir/char-embeddings |
186 |
|
0 |
0 |
about 9 years ago |
0 |
|
3 |
mit |
Python |
| A repository containing 300D character embeddings derived from the GloVe 840B/300D dataset, and uses these embeddings to train a deep learning model to generate Magic: The Gathering cards using Keras |
| cbaziotis/datastories-semeval2017-task4 |
171 |
|
0 |
0 |
almost 8 years ago |
0 |
|
8 |
mit |
Python |
| Deep-learning model presented in "DataStories at SemEval-2017 Task 4: Deep LSTM with Attention for Message-level and Topic-based Sentiment Analysis". |
| AlexGidiotis/Document-Classifier-LSTM |
167 |
|
0 |
0 |
over 2 years ago |
0 |
|
3 |
mit |
Python |
| A bidirectional LSTM with attention for multiclass/multilabel text classification. |
| ezgisubasi/turkish-tweets-sentiment-analysis |
52 |
|
0 |
0 |
over 2 years ago |
0 |
|
1 |
|
Jupyter Notebook |
| This sentiment analysis project determines whether the tweets posted in the Turkish language on Twitter are positive or negative. |
| jfilter/text-classification-keras |
50 |
|
2 |
0 |
almost 6 years ago |
6 |
April 28, 2019 |
0 |
mit |
Python |
| 📚 Text Classification Library with Keras |
