neuralconvo - Neural conversational model in Torch

•    Lua

This is an attempt at implementing Sequence to Sequence Learning with Neural Networks (seq2seq) and reproducing the results in A Neural Conversational Model (aka the Google chatbot). Human: What is the purpose of living? Machine: To live forever.

cakechat - CakeChat: Emotional Generative Dialog System

•    Python

It is written in Theano and Lasagne. It uses end-to-end trained embeddings of 5 different emotions to generate responses conditioned by a given emotion. The code is flexible and allows to condition a response by an arbitrary categorical variable defined for some samples in the training data. With CakeChat you can, for example, train your own persona-based neural conversational model[5] or create an emotional chatting machine without external memory[4].

DeepQA - My tensorflow implementation of "A neural conversational model", a Deep learning based chatbot

•    Python

This work tries to reproduce the results of A Neural Conversational Model (aka the Google chatbot). It uses a RNN (seq2seq model) for sentence predictions. It is done using python and TensorFlow. The loading corpus part of the program is inspired by the Torch neuralconvo from macournoyer.

Screenshot-to-code-in-Keras - A neural network that transforms a screenshot into a static website

•    Jupyter

This is the code for the article 'Turning design mockups into code with deep learning' on FloydHub's blog. Within three years deep learning will change front-end development. It will increase prototyping speed and lower the barrier for building software.

practical-pytorch - PyTorch tutorials demonstrating modern techniques with readable code

•    Jupyter

These tutorials have been merged into the official PyTorch tutorials. Please go there for better maintained versions of these tutorials compatible with newer versions of PyTorch. Learn PyTorch with project-based tutorials. These tutorials demonstrate modern techniques with readable code and use regular data from the internet.

TensorFlow-Tutorials - 텐서플로우를 기초부터 응용까지 단계별로 연습할 수 있는 소스 코드를 제공합니다

•    Python

텐서플로우를 기초부터 응용까지 단계별로 연습할 수 있는 소스 코드를 제공합니다. 텐서플로우 공식 사이트에서 제공하는 안내서의 대부분의 내용을 다루고 있으며, 공식 사이트에서 제공하는 소스 코드보다는 훨씬 간략하게 작성하였으므로 쉽게 개념을 익힐 수 있을 것 입니다. 또한, 모든 주석은 한글로(!) 되어 있습니다.

delta - DELTA is a deep learning based natural language and speech processing platform.

•    Python

DELTA is a deep learning based end-to-end natural language and speech processing platform. DELTA aims to provide easy and fast experiences for using, deploying, and developing natural language processing and speech models for both academia and industry use cases. DELTA is mainly implemented using TensorFlow and Python 3. For details of DELTA, please refer to this paper.

kaggle-web-traffic - 1st place solution

•    Jupyter

1st place solution

seq2seq-signal-prediction - Signal prediction with a Sequence-to-Sequence (seq2seq) Recurrent Neural Network (RNN) model in TensorFlow - Guillaume Chevalier

•    Jupyter

The goal of this project of mine is to bring users to try and experiment with the seq2seq neural network architecture. This is done by solving different simple toy problems about signal prediction. Normally, seq2seq architectures may be used for other more sophisticated purposes than for signal prediction, let's say, language modeling, but this project is an interesting tutorial in order to then get to more complicated stuff. Except the fact I made available an ".py" Python version of this tutorial within the repository, it is more convenient to run the code inside the notebook. The ".py" code exported feels a bit raw as an exportation.

Seq2Seq-PyTorch - Sequence to Sequence Models with PyTorch

•    Python

A vanilla sequence to sequence model presented in https://arxiv.org/abs/1409.3215, https://arxiv.org/abs/1406.1078 consits of using a recurrent neural network such as an LSTM (http://dl.acm.org/citation.cfm?id=1246450) or GRU (https://arxiv.org/abs/1412.3555) to encode a sequence of words or characters in a source language into a fixed length vector representation and then deocoding from that representation using another RNN in the target language. An extension of sequence to sequence models that incorporate an attention mechanism was presented in https://arxiv.org/abs/1409.0473 that uses information from the RNN hidden states in the source language at each time step in the deocder RNN. This attention mechanism significantly improves performance on tasks like machine translation. A few variants of the attention model for the task of machine translation have been presented in https://arxiv.org/abs/1508.04025.

sockeye - Sequence-to-sequence framework with a focus on Neural Machine Translation based on Apache MXNet

•    Python

Felix Hieber, Tobias Domhan, Michael Denkowski, David Vilar, Artem Sokolov, Ann Clifton and Matt Post (2017): Sockeye: A Toolkit for Neural Machine Translation. In eprint arXiv:cs-CL/1712.05690.If you are interested in collaborating or have any questions, please submit a pull request or issue. You can also send questions to sockeye-dev-at-amazon-dot-com.

pytorch-seq2seq - pytorch-seq2seq is a framework for sequence-to-sequence (seq2seq) models in PyTorch

•    Python

This is a framework for sequence-to-sequence (seq2seq) models implemented in PyTorch. The framework has modularized and extensible components for seq2seq models, training and inference, checkpoints, etc. This is an alpha release. We appreciate any kind of feedback or contribution. This package requires Python 2.7 or 3.6. We recommend creating a new virtual environment for this project (using virtualenv or conda).

tf_seq2seq_chatbot - [unmaintained]

•    Python

Note: the repository is not maintained. Feel free to PM me if you'd like to take up the maintainance. Build a general-purpose conversational chatbot based on a hot seq2seq approach implemented in tensorflow. Since it doesn't produce good results so far, also consider other implementations of seq2seq.

OpenSeq2Seq - Toolkit for efficient experimentation with various sequence-to-sequence models

•    Python

This is a research project, not an official NVIDIA product. OpenSeq2Seq main goal is to allow researchers to most effectively explore various sequence-to-sequence models. The efficiency is achieved by fully supporting distributed and mixed-precision training. OpenSeq2Seq is built using TensorFlow and provides all the necessary building blocks for training encoder-decoder models for neural machine translation and automatic speech recognition. We plan to extend it with other modalities in the future.

seq2seq-couplet - Play couplet with seq2seq model. 用深度学习对对联。

•    Python

This is a project use seq2seq model to play couplets (对对联)。 This project is written with Tensorflow. You can try the demo at https://ai.binwang.me/couplet. You will need some data to run this program, the dataset can be downloaded from this project.

debug_seq2seq - [unmaintained] Make seq2seq for keras work

•    Python

Note: the repository is not maintained. Feel free to PM me if you'd like to take up the maintainance. Make seq2seq for keras work. And also give a try to some other implementations of seq2seq.

seq2seq_chatbot_links - Links to the implementations of neural conversational models for different frameworks

Links to the implementations of neural conversational models for different frameworks. Contributions are welcomed. A dialog system that is able to express emotions in a text conversation. See online demo.

seq2seq - Minimal Seq2Seq model with Attention for Neural Machine Translation in PyTorch

•    Python

Minimal Seq2Seq model with attention for neural machine translation in PyTorch. This implementation relies on torchtext to minimize dataset management and preprocessing parts.

tensorflow-shakespeare - Neural machine translation between the writings of Shakespeare and modern English using TensorFlow

•    Python

This is an example of using the new Google's TensorFlow library on monolingual translation going from modern English to Shakespeare based on research from Wei Xu. Delete /cache to start anew.

tf_tutorial_plus - Tutorials for TensorFlow APIs the official documentation doesn't cover

•    Jupyter

Tutorials for TensorFlow APIs the official documentation doesn't cover