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
Classifies the movie reviews as positive or negative using LSTM Networks
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).
TV Script Generation with RNN with Udacity
Telugu OCR using RNN
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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