Implementing Recurrent Neural Network from Scratch
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Updated
May 28, 2018 - Python
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).
Implementation of Music Generation in PyTorch
Imageboard bot with recurrent neural network (RNN, GRU)
RNN-based language models in pytorch
Char RNN Language Model based on Tensorflow
BlackOut and Adaptive Softmax for language models by Chainer
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.
attempt at implementing "Memory Architectures in Recurrent Neural Network Language Models" as a part of the ICLR 2018 reproducibility challenge
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).
Code and scripts for training, testing and sampling auto-regressive recurrent language models on PyTorch with RNN, GRU and LSTM layers
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. (only supports Bahdanau Attention right now).
Classifies the movie reviews as positive or negative using LSTM Networks
Telugu OCR using RNN
TV Script Generation with RNN with Udacity
RNN-LSTM model that classifies movie reviews
Harnessing NLP for Historical Text Analysis: A Case Study on the Indus Valley Civilization
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