Compared to conventional and classical signal processing algorithms, the process done in Deep Neural Networks (DNNs) resembles more to what is happening in human brain and because of that, these networks can provide more useful insight and perception form the surrounding environment. Moreover, by following a teacher like a crew member, they can learn by themselves to how to react autonomously in different and complex situations. All these properties make them a valuable technology to help NASA automates earth and space missions. Here are some of the applications of DNN in NASA-related missions: - Aircraft control - Damage-adaptive decision making - Detect extreme weather in climate datasets - Classification of aerial images - Fire detection and control - Autonomous driving of vehicles for space missions - Automatic feature extraction from large datasets of probes images
Deep neural networks are envisioned to revolutionize the field of machine learning and their applications because they provide a simple platform to achieve performances beyond what could have been achieved with conventional digital Von Neumann architectures. In fact, despite of being a very young technology, it is already in use in so many commercial applications including but not limited to: - Google is using DNNs for speech recognition in Alexa, for image recognition to diagnose diseases from medical images, for object detection in its self-driving cars, for translating text from one language to another one, etc. - Baidu is using DNN to automatically convert speech to text in mobile phones - The hand-written text on all checks and envelops are automatically read with DNNs - In the huge market of advertising, DNN are now helping to identify the potential costumers for a particular product - All face recognitions and people identifications happening in Facebook pages are possible only because of DNNs - Automatic game playing - Automatic image caption generation
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