Implementing Recurrent Neural Network from Scratch

Keras implementations of three language models: character-level RNN, word-level RNN and Sentence VAE (Bowman, Vilnis et al 2016).

RNN-based language models in pytorch

s-atmech is an independent Open Source, Deep Learning python library which implements attention mechanism as a RNN(Recurrent Neural Network) Layer as Encoder-Decoder system. (Supports all Models both Luong and Bhanadau).

Char RNN Language Model based on Tensorflow

BlackOut and Adaptive Softmax for language models by Chainer

Implementation of Music Generation in PyTorch

attempt at implementing "Memory Architectures in Recurrent Neural Network Language Models" as a part of the ICLR 2018 reproducibility challenge

Telugu OCR using RNN

TV Script Generation with RNN with Udacity

Code and scripts for training, testing and sampling auto-regressive recurrent language models on PyTorch with RNN, GRU and LSTM layers

Character Encoding RNN

RNN-LSTM model that classifies movie reviews

Harnessing NLP for Historical Text Analysis: A Case Study on the Indus Valley Civilization

Classifying whether a disaster tweet is real or not using LSTM and GloVe word embeddings

The main task of the character-level language model is to predict the next character given all previous characters in a sequence of data, i.e. generates text character by character.

A lightweight, deep learning library written in pure Python

Imageboard bot with recurrent neural network (RNN, GRU)