Topic: "text-generation-using-rnn"
dhairya0907/Creepypasta-Text-Generator
Website which uses Deep Learning to generate horror stories.
Language: Jupyter Notebook - Size: 668 MB - Last synced at: 2 months ago - Pushed at: almost 4 years ago - Stars: 6 - Forks: 1

shreyansh26/Sentence-VAE
A re-implementation of the Sentence VAE paper, Generating Sentences from a Continuous Space
Language: Python - Size: 35 MB - Last synced at: 3 months ago - Pushed at: over 2 years ago - Stars: 3 - Forks: 1

midusi/freestyle_generator
Generador de freestyle basado en batallas de RAP
Language: Jupyter Notebook - Size: 243 MB - Last synced at: 3 months ago - Pushed at: over 4 years ago - Stars: 3 - Forks: 1

dhairya0907/Creepypasta-Text-Generator-Website
Website which uses Deep Learning to generate horror stories.
Language: JavaScript - Size: 83.5 MB - Last synced at: 3 months ago - Pushed at: almost 4 years ago - Stars: 2 - Forks: 1

chandan047/GCN-GLAC
Graph convolution-based visual storytelling
Language: Jupyter Notebook - Size: 10.3 MB - Last synced at: about 2 years ago - Pushed at: over 4 years ago - Stars: 2 - Forks: 2

OMEGAMAX10/Text-Generation-with-LSTM
Text generation using a character-based RNN with LSTM cells. We will work with a dataset of Shakespeare's writing from Andrej Karpathy's The Unreasonable Effectiveness of Recurrent Neural Networks. Given a sequence of characters from this data ("Shakespear"), train a model to predict the next character in the sequence ("e"). Longer sequences of text can be generated by calling the model repeatedly. Developed using Keras. Inspired by the following notebook: https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/tutorials/text/text_generation.ipynb#scrollTo=BwpJ5IffzRG6
Language: Python - Size: 8.79 KB - Last synced at: over 1 year ago - Pushed at: over 4 years ago - Stars: 2 - Forks: 0

Harshraj1301/Generating-Text-Using-LSTM
Developed an LSTM model to generate text, mimicking the style of Nietzsche's writings
Language: Jupyter Notebook - Size: 18.6 KB - Last synced at: about 1 year ago - Pushed at: about 1 year ago - Stars: 1 - Forks: 0

batuhan3526/NLP-Tutorial-and-Text-Generation
Language: Jupyter Notebook - Size: 1.62 MB - Last synced at: over 2 years ago - Pushed at: almost 5 years ago - Stars: 1 - Forks: 0

alekswael/text_generation_with_RNNs
This repo contains a collection of scripts for builiding a text generator by training a recurrent neural network on a large text dataset.
Language: Python - Size: 24.4 KB - Last synced at: almost 2 years ago - Pushed at: almost 2 years ago - Stars: 0 - Forks: 0

mpolinowski/tf-rnn-text-generator
Generate text using a character-based RNN
Language: Python - Size: 2.93 KB - Last synced at: 3 months ago - Pushed at: over 2 years ago - Stars: 0 - Forks: 0

probabilityhill/seq2seq-gru-pytorch
PyTorchのGRUを用いてseqence-to-seqenceを実装
Language: Python - Size: 6.42 MB - Last synced at: over 2 years ago - Pushed at: about 3 years ago - Stars: 0 - Forks: 1

fulcrum101/Star_Wars_text_generator
Text GAN trained on Star Wars episode IV script. All information taken from official TensorFlow tutorial page. Char-embedded.
Language: Jupyter Notebook - Size: 127 KB - Last synced at: 2 months ago - Pushed at: over 3 years ago - Stars: 0 - Forks: 0

kashish45/Text-Generation-Using-Tensorflow
Language: Jupyter Notebook - Size: 32.2 KB - Last synced at: about 1 year ago - Pushed at: over 4 years ago - Stars: 0 - Forks: 0

mitya8128/text_gen
poems text generation
Language: Jupyter Notebook - Size: 3.28 MB - Last synced at: 10 months ago - Pushed at: over 4 years ago - Stars: 0 - Forks: 0

kanchan88/eminem-rap-generator-using-RNN
RNN is one of the very powerful deep-learning algorithm which works amazingly well on Sequential Data. As historical or past data plays major role in the prediction of sequential data, RNN takes these inputs of not only recent output but also past output. Here I have used GRU for the prediction of eminem's Rap.
Language: Jupyter Notebook - Size: 20.1 MB - Last synced at: over 1 year ago - Pushed at: almost 5 years ago - Stars: 0 - Forks: 0
