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cakechat - CakeChat: Emotional Generative Dialog System
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].
conversational-ai conversational-agents conversational-bots dialogue-agents dialogue-systems dialog-systems nlp deep-learning seq2seq seq2seq-chatbot seq2seq-model theano lasagneFacebook-Messenger-Bot - Facebook chatbot that I trained to talk like me using Seq2Seq
The FB Messenger chatbot that I trained to talk like me. The associated blog post. For this project, I wanted to train a Sequence To Sequence model on my past conversation logs from various social media sites. You can read more about the motivation behind this approach, the details of the ML model, and the purpose of each Python script in the blog post, but I want to use this README to explain how you can train your own chatbot to talk like you.
chatbot seq2seq-model facebook-messenger-bot tensorflowSomiao-Pinyin - Somiao Pinyin: Train your own Chinese Input Method with Seq2seq Model 搜喵拼音输入法
Original code in https://github.com/Kyubyong/neural_chinese_transliterator for research purpose. This repository intends to experiment with different training data and interactive user inputs, and possibly develop towards a real data-personalized and model-localized Pinyin Input product.
chinese seq2seq-model pinyin input-methodBitcoin-Price-Prediction - Predict Bitcoin Price using RNN
Predict Bitcoin Price using RNN
bitcoin-api python-library rnn-tensorflow numpy seq2seq-model
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