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SAS: The Unreasonable Benefits of Deep Learning

July 28, 2016 | Business

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Earlier this month, Dan gave a talk at Sentiment Analysis Symposium discussing why businesses should consider adopting deep learning solutions. His slides and a video of the presentation are available for those of you who couldn’t make it — if you have any questions, click the little chat bubble at the bottom right hand corner of your screen, or reach out to us at contact@indico.io.

About the Session

Machine learning is becoming the tool of choice for analyzing text and image data. While traditional text processing solutions rely on the ability of experts to encode domain knowledge, machine learning models learn this directly from the data. Deep learning is a branch of machine learning that like the human brain quickly learns hierarchical representations of concepts, and it has been key to unlocking state-of-the-art results on a range of text and image classification tasks such as sentiment analysis and beyond.

In this session, we will:

  • Show the impact of a deep learning based approach over NLP and traditional machine learning based methods for text analysis across key dimensions such as accuracy, flexibility, and the amount of required training data
  • Discuss how deep learning models are now setting the records for state-of-the-art accuracy in sentiment analysis
  • Demonstrate the flexibility of this approach by showing how the features learned by one model can be easily reused in different domains (e.g., handling additional languages, or predicting new categories) to drastically reduce the time to deployment
  • Touch on the ability of this method to handle additional types of data beyond text, e.g, images, for maximum insight

The Unreasonable Advantages of Deep Learning (Dan Kuster, indico) from Seth Grimes on Vimeo.

About the Conference

“What are your customers thinking … and saying, tweeting, and posting? The Sentiment Analysis Symposium is the first and best conference to address the business value of sentiment, opinion, emotion, and intent in online, social and enterprise data. Our audience is comprised of business analysts, developers, researchers, and solution providers.”
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