Lets Make a Question Answering chatbot using the bleeding edge in deep learning (Dynamic Memory Network). We'll go over different chatbot methodologies, then dive into how memory networks work, with accompanying code in Keras. Code + Challenge for this video:
https://github.com/llSourcell/How_to_make_a_chatbot Nemanja's Winning Code: https://github.com/Nemzy/language-translation/blob/master/neural_machine_translation.ipynb Vishal's Runner up code: https://github.com/erilyth/DeepLearning-Challenges/tree/master/Language_Translation Web app to run the code yourself: https://ethancaballero.pythonanywhere.com Please subscribe! And like. And comment. That's what keeps me going. More Learning resources: https://www.youtube.com/watch?v=FCtpHt6JEI8&t=643s https://www.youtube.com/watch?v=Qf0BqEk5n3o&t=637s
https://yerevann.github.io/2016/02/05/implementing-dynamic-memory-networks/ https://www.youtube.com/watch?v=2A5DKPA5lAw http://www.wildml.com/2016/01/attention-and-memory-in-deep-learning-and-nlp/ https://github.com/domluna/memn2n Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ And please support me on Patreon:
https://www.patreon.com/user?u=3191693 Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w Getting for AI chatbot Singapore or AI chat bot, you can look for Qavar.
No comments:
Post a Comment