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Tuesday, February 27 2018
6:30pm - 8:30pm

Content Classification AI 2 Ways: Few-Shot Learning and Keras Deep Learning

6:30 Networking and food (food sponsored by 7:00 Katherine Bailey, Principal Data Scientist at Acquia Classifying Content Using Few-Shot Learning Text classification is usually treated as a supervised learning problem: given a set of labeled texts, train a classifier such that it can accurately predict the classes of unseen texts. But many businesses have large amounts of text data where few or no texts have been classified. This talk will introduce a way of classifying an entire set of unlabeled texts using a human-in-the-loop approach, where the content owner manually classifies just one or two items per category and the rest can be automatically classified. The technique uses transfer learning (in the form of pre-trained word embeddings), human-in-the-loop ML and “few-shot” learning. 7:30 Sara Robinson, Developer Advocate at GoogleJosh Gordon, Developer Advocate at Google Building a Custom Text Classification Model With Keras Most businesses deal with tons of text data on a daily basis. In this talk, we'll show you how to build a machine learning model that tags text into custom categories specific to your dataset. We'll build our model with Keras, a high-level API for TensorFlow. We'll start by giving an overview of TensorFlow and Keras, and then we'll dive into a live demo of our text classification model. 8:00 Networking and wrap up 8:30 End

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