Deep learning uses deep and wide artificial neural networks trained on massive datasets to teach computers how to solve perceptual problems, such as detecting recognizable objects in images, translating or understanding natural languages, and transcribing speech. Deep learning is used in many industries today to automate perceptual tasks at near-human levels of performance. Practical examples include vehicle, pedestrian and landmark identification for driver assistance; face recognition in social media images; speech recognition for digital personal assistants; machine translation; and cancer detection in medical images. These same game-changing technologies have broad applicability to many geospatial intelligence (GEOINT) tasks. In this webinar we will give a brief introduction to deep learning and take a tour of the current and potential applications of deep learning to a variety of GEOINT tasks.

3 key takeaways: (1) Deep learning is applicable to a broad range of challenging perceptual GEOINT tasks; (2) Deep learning is accessible today through open-source software frameworks accelerated by GPU technology and; (3) Deep learning is massively scalable to thrive on web-scale datasets and rapidly generalize to new problem sets.

Register today! 

Webinar Date:  Tuesday, August 18, 2015

Webinar Time:  10:00am - 11:00am (Pacific)


 

Applications of Deep Learning to GEOINT Missions

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Presenter:

Jon Barker, Solution Architect, NVIDIA

Presenter Bio:

Jon Barker is a Washington DC based Solution Architect with NVIDIA focused on applications of GPU accelerated machine learning and data analytics to defense, intelligence, and national security. Prior to joining NVIDIA, Jon spent almost a decade as a government research scientist within the UK Ministry of Defense and the US Department of Defense R&D communities. Whilst in government service, Jon led R&D projects in sensor data fusion, big data analtyics, and machine learning for multi-modal sensor data to support military situational awareness and aid decision making. Jon has a PhD and BSc in Pure Mathematics from the University of Southhampton, UK.