Deep Learning? Sometimes It Pays To Go Shallow - AI Summary

If this is the case, deep learning probably isn't the solution you need, but you can still draw on machine learning to get results. Because these models are so specific, unlike the more generalized deep learning models, they can be trained on much smaller amounts of data -- think hundreds of thousands or millions of documents instead of billions. Another example of an ML solution in pharma that doesn't require internet-scale deep learning comes from work that our company, Lexalytics, has done with Biogen Japan and its Medical Information Department (MID). We used Biogen's data to train and deploy custom machine learning models into the underlying NLP; the resulting system now understands complex relationships between conditions, ailments, drugs, issues, therapies, and other entities and products. Deep learning techniques require phenomenal investment as well as access to enormous amounts of data, which, for most business problems, simply isn't feasible.