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Notes, thoughts, and practice of applied machine learning. Charles H Martin, PhD. But why is Deep Learning so successful? Is Deep Learning just old-school Neural Networks on modern hardware? Is it just that we have so much data now the methods work better? Is Deep Learning just a really good at finding features. Researchers are working hard to sort this out.
Why do models need to be interpretable? The primary reason why we need explainability in AI, is to develop a sense of understanding. Word2Vec is currently one of the best algorithms for computing word-embeddings. Given a significant amount of text. It was originally trained on a 3 million-word corpus of Google News articles.
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The site had the following in the homepage, "KDD Cup 2015 The story of how I built hundreds of predictive models." I noticed that the web site stated "And got so close, yet so far away from 1st place! Data Until I Die! The challenge from the KDD Cup this year was to use their data relating to student enrollment in online MOOCs to predict who would drop out vs who would stay." They also stated " The short story is that using H2O and a lot of my free time, I trained several hundred GBM models looking for the final one which eventually got me an AUC score of 0. 88127 on the KDD Cup leaderboard."ANALYZE MORE BUSINESSES
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