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christan's Projects

abstractive-summarization icon abstractive-summarization

Implementation of abstractive summarization using LSTM in the encoder-decoder architecture with local attention.

ai-for-medicine icon ai-for-medicine

This repo contains my assignment solutions of AI for Medicine Specialization on Coursera

ai-for-medicine-specialization icon ai-for-medicine-specialization

I have completed this specialization from Coursera by deeplearning.ai. I have uploaded the solutions of the assignments in this repo.

artificial-intelligence-deep-learning-machine-learning-tutorials icon artificial-intelligence-deep-learning-machine-learning-tutorials

A comprehensive list of Deep Learning / Artificial Intelligence and Machine Learning tutorials - rapidly expanding into areas of AI/Deep Learning / Machine Vision / NLP and industry specific areas such as Climate / Energy, Automotives, Retail, Pharma, Medicine, Healthcare, Policy, Ethics and more.

awesome-dl4nlp icon awesome-dl4nlp

A curated list of awesome Deep Learning for Natural Language Processing resources

awesome-object-detection icon awesome-object-detection

Awesome Object Detection based on handong1587 github: https://handong1587.github.io/deep_learning/2015/10/09/object-detection.html

chatbot icon chatbot

In this project, we will build a chatbot using conversations from Cornell University's Movie Dialogue Corpus. The main features of our model are LSTM cells, a bidirectional dynamic RNN, and decoders with attention. The conversations will be cleaned rather extensively to help the model to produce better responses. As part of the cleaning process, punctuation will be removed, rare words will be replaced with "UNK" (our "unknown" token), longer sentences will not be used, and all letters will be in the lowercase. With a larger amount of data, it would be more practical to keep features, such as punctuation. However, I am using FloydHub's GPU services and I don't want to get carried away with too training for too long.

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