Machine Learning - Four Context

Machine Learning

Final Thoughts About RelevantXKCD project

Part of my RelevantXKCD project writeup After I wrapped a simple Django server and posted it on Reddit in the subreddit /r/xkcd, I got some great feedback! For the Model As /u/drcopus helpfully enlightened me, I should've done data augmentation on the original training set. I

Machine Learning

Loading the Model (and Complaining about Memory Usage)

How I loaded the files in python I used joblib to save the files from sklearn.externals import joblib feature_list = count_vect.get_feature_names() model = "model105" joblib.dump(feature_list, model + '_vocabulary.pkl') joblib.dump(tfidf_transformer, model + '_transform.pkl') joblib.dump(

Machine Learning

Finding the Right Model (Multinomial NB, RNN, SGDClassifier)

Part of this entire writeup Goal Find a model to accurately predict a relevant XKCD given multiple words (a reddit comment). This essentially gave me a bunch of positively single-labeled data since each comment usually only had one "relevant XKCD" sub-comment attached. Example: Searching "Python" would

Machine Learning

Reddit Relevant XKCD

So I embarked on a machine learning quest as a senior project. With all these advances in AI, I wanted to have some fun myself :P. You can view it here. I was inspired by Dan Zhang and Megan Ruthven's Relevant XKCD finder. They used ExplainXKCD to populate