Facebook-Messenger-Bot
TensorLayer
Facebook-Messenger-Bot | TensorLayer | |
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2 | 1 | |
708 | 7,275 | |
- | 0.0% | |
0.0 | 0.0 | |
5 months ago | about 1 year ago | |
Python | Python | |
MIT License | GNU General Public License v3.0 or later |
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Facebook-Messenger-Bot
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Help trying to get a chat bot to talk like me
This is what im currently using, and this is what im looking at.
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Any resources to mimic the way someone talks?
Im currrently using this method which produces less than optimal results to generate a response similar to those of another. I tried looking this up in google in a bid to find some papers to little avail. Most of the results are mimicking the *voice* of another, not parlance of another.
TensorLayer
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Introduction to ‘TensorLayer’: A Python-based Versatile Deep Learning Library Designed for Machine Learning Researchers
Github: https://github.com/tensorlayer/TensorLayer
What are some alternatives?
ChatterBot - ChatterBot is a machine learning, conversational dialog engine for creating chat bots
nngen - NNgen: A Fully-Customizable Hardware Synthesis Compiler for Deep Neural Network
maubot - A plugin-based Matrix bot system.
chainerrl - ChainerRL is a deep reinforcement learning library built on top of Chainer.
chatbot-with-algolia-answers - Chatbot with Algolia Answers and Dialogflow
gpt-j-chatbot - A GPT-J Chatbot Template for creating AI Characters (Virtual Girlfriend Chatbot, Replika-esque)
python-telegram-bot - We have made you a wrapper you can't refuse
yagooglesearch - Yet another googlesearch - A Python library for executing intelligent, realistic-looking, and tunable Google searches.
SRGAN - Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
biprop - Identify a binary weight or binary weight and activation subnetwork within a randomly initialized network by only pruning and binarizing the network.
quickai - QuickAI is a Python library that makes it extremely easy to experiment with state-of-the-art Machine Learning models.
TicTacToe - Tic Tac Toe game, designed to be used to train a Deep Neural Network via Reinforcement Learning (DQN). It can also be played by 2 humans and features a hard coded AI that never looses and will win if you do not do perfect play against it.