Practical considerations when shifting to using deep learning for your text data

Emmanuel Ameisen and Yan Kou share a guide for moving your company toward deep learning using a collection of NLP best practices gathered from conversations with 75+ teams from Google, Facebook, Amazon, Twitter, Salesforce, Airbnb, Capital One, Bloomberg, and others.

Most companies in industry collect and leverage text data for some part of their business operations. Some, such as Yelp and Twitter, have text data at the core of their platform while most others utilize it behind the scenes, triaging and responding to support requests and customer feedback. Top companies have achieved incredible performance by switching to deep learning methods for text analysis. Companies making this shift, though, typically encounter a set of challenges which include determining which models to spend their time and money on, how to validate and explain model performance, and how model complexity affects the ease of deploying them. Examples of such business challenges include: Drawing on research gathered from conversations with 75+ teams from Google, Facebook, Amazon, Twitter, Salesforce, Airbnb, Capital One, Bloomberg, and others, Emmanuel Ameisen and Yan Kou share a guide for moving your company from traditional machine learning approaches, such as logistic regression on bag-of-words features to more expressive deep learning models, such as convolutional neural networks and recurrent neural networks. These new techniques allow companies to improve many of the core algorithmic concerns that underlie a majority of key business operations, such as clustering (e.g., to identify topics in articles) and classification (e.g., to automatically forward support requests to the appropriate person). You’ll learn the trade-offs of different models in terms of power, complexity, and interpretability and understand how to choose the ones most appropriate for your projects.