How to Overcome the Challenges
of a Computer Vision Project

Available on-demand

 

From recognizing faces to processing the live action of a football game, computer vision opens many exciting possibilities. Find out what it takes to really capitalize on advancements in AI.

About the webinar

Many Data Scientists tend to get very excited about deep learning. And why not? It has strongly contributed to the further development of game-changing applications like computer vision or Neural Language Processing.

However, before starting any computer vision endeavours, it pays off to zoom in on some basics: Using a repeatable end-to-end process – to guide you through image pre-processing, deep learning and productionising your models - will help ensure your deep learning models are as accurate as possible and take your models out of the lab and drive actual business value. 

Why attend?

  • Find out how Computer Vision works
  • Learn about the prerequisites for computer vision applications
  • Understand how image pre-processing, deep learning and productionising your models will drive business value and success

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About the Expert


Matthew Stainer

Principal Data Scientist, SAS UK & Ireland

A Principal Data Scientist at SAS UKI, supporting customers to enhance their businesses decision making. He works across the analytical technologies including data mining and forecasting. His particular areas of interest and expertise is text analysis, identifying the impact of analytical solutions on business processes and communicating the results of analysis to a business audience. He has specific experience in Customer Management solutions, including Complaints Analysis, NPS, Customer Analysis, Optimisation, Decision Management, Recommendation Engines, Campaign Management, Debt Management and Web Analytics. He has been at SAS for 10 years and the software industry for over 25, working across a variety of vertical sectors including retail banking, insurance, healthcare, retail, media and telecommunications.

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