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Deep Learning in Agriculture

A confluence of factors have converged to afford the opportunity to apply data science at large scale to agricultural production. The demand for agricultural outputs is growing and there is a need to meet this demand by utilizing increasingly mechanized precision agriculture together with the enormous data volumes collected to intelligently optimize agriculture outputs. The Climate Corporation will lead a discussion on the breadth and depth of machine learning challenges, and in particular applications of deep learning methods,  to tackling the world’s largest optimization problem: optimizing global food production. 

Speaker:

Erik Andrejko leads The Climate Corporation Science and Research Organization, spanning research across research teams including Climatology, producing hyper-local weather forecasts, and Agronomic models, connecting hyper- local weather measurements to agronomic outcomes. Previously, Erik worked at several Bay Area start-ups solving large - scale Statistical Machine Learning problems. Erik holds a B.S. in Computer Science from Arizona State University and a PhD in Mathematics from University of Wisconsin -  Madison.






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  • Olivier J.

    Good overview of challenge and big data platform... not clear what they do though... actual examples would have helped

    April 24, 2014

  • A former member
    A former member

    1 · March 30, 2014

  • JeffD

    I guess I wish it had been made clearer that this meetup was about /what we think we might be able to do/ instead of /what we've done/, though admittedly it does say 'discuss challenges'. It was nevertheless still interesting.

    1 · March 29, 2014

  • Amir Y.

    Could not make it this time. Any recording/slides to post?

    March 28, 2014

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