The Deep Learning Society: Difference between revisions
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Client: Morgan Stanley | Client: Yana Afanasyeva, [[Morgan Stanley]] <Yana.Afanasyeva@morganstanley.com> | ||
Technology companies invest billions in self-driving cars and self-playing computer games, but surprisingly little in real social problems. Your task is to use the latest deep learning technologies to create an intelligent social work assistant that can recognise and act in situations of real need. | Technology companies invest billions in self-driving cars and self-playing computer games, but surprisingly little in real social problems. Your task is to use the latest deep learning technologies to create an intelligent social work assistant that can recognise and act in situations of real need. Your client will provide a GPU-accelerated system suitable for use with deep learning frameworks like Google TensorFlow. You will train it using data from online social networks such as MumsNet, to recognise and anticipate situations where people are going to use words like “hopeless”, “depressed” or “suicide". With the help of deep learning, even a simple Bag of Words, together with metadata such as time of day, location and comment feedback will be sufficient to recognise trigger conditions in large data sets and mobilise assistance. | ||
Latest revision as of 09:44, 9 December 2016
Client: Yana Afanasyeva, Morgan Stanley <Yana.Afanasyeva@morganstanley.com>
Technology companies invest billions in self-driving cars and self-playing computer games, but surprisingly little in real social problems. Your task is to use the latest deep learning technologies to create an intelligent social work assistant that can recognise and act in situations of real need. Your client will provide a GPU-accelerated system suitable for use with deep learning frameworks like Google TensorFlow. You will train it using data from online social networks such as MumsNet, to recognise and anticipate situations where people are going to use words like “hopeless”, “depressed” or “suicide". With the help of deep learning, even a simple Bag of Words, together with metadata such as time of day, location and comment feedback will be sufficient to recognise trigger conditions in large data sets and mobilise assistance.
