IBM has announced the launch of Deep Learning as a Service, which enables organizations to overcome the common barriers to deep learning deployment: skills, standardization, and complexity. It embraces a wide array of popular open source frameworks like TensorFlow, Caffe, PyTorch and others, and offers them as a cloud-native service on IBM Cloud, lowering the barrier to entry for deep learning.
Deep learning involves building and training a “neural network,” a machine learning model inspired by the human brain. Once a neural network is trained on a dataset, it can be used for a variety of recognition tasks —from identifying objects in an image and recognizing intention in an expression, to recognizing trends in a set of data.
For example, deep learning can help an insurance company determine how much a car has been damaged after an accident. How? An image of the damaged car can be included within a dataset trained at detecting not only the car make and model, but also where the car has sustained damage. Once the deep learning AI system recognizes the car, it compares the image of the damaged car to its dataset —and then classifies that damaged car as, for example, missing a bumper.
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