# Moving Deep Learning to the Edge
## 3 Deep Learning
### Deep Learning Applications  
Deep learning is applied in a vast set of fields. With the migration of deep learning to the edge, a new set of applications with latency constraints are now possible:  
* Image and video: Most deep neural networks are associated with image classification [51; 52] and object detection [53; 54]. These are used for image and video analysis in a vast set of applications, like video surveillance, obstacle detection and face recognition. Many of these applications require real-time analysis and therefore must be executed at the edge. Wireless cameras are frequently used to detect objects and people [55; 56]. Considering the computational limitations of edge platforms, some solutions propose an integrated solution between edge and cloud computing [57] that can guarantee high accuracy with low latency. Some commercial solutions [58] adopt this integrated solution, where the local edge device only forwards images to the cloud if it is classified locally as important.
* Natural language: Natural language processing [59; 60] and machine translation [61; 62] have also adopted DNNs to improve the accuracy. Applications like query and answer require low latency between the query and the answer to avoid unwanted idle times. Well-known examples of natural language processing are the voice assistants Alexa from Amazon [63] and Siri from Apple [64; 65]. These voice assistants have an integrated solution with a local simple network at the edge to detect wake words. When detected, the systems record the voice and sends it to the cloud to process and generate a response.
* Smart home and smart city: The smart home is a solution that collects and processes data from home devices and human activity and provides a set of services to improve home living. Some examples are indoor localization [66], home robotics [67], and human activity monitoring [68]. This smart concept was extended to cities to improve many aspects of city living [69], like traffic flow management [70]. Data relative to the traffic state from different roads can be used to predict the flow of roads and suggest some alternative roads [71]. Object detection is also used to track incidents on the roads and warn the drivers about alternatives to avoid an incident [72].
* Medical: Medical is another field were DNNs are being applied successfully to predict diseases and to analyze images. In the genetics area, DNN has been successful to extract hidden features from genetic information that allows us to predict diseases, like autism [73; 74]. Medical imaging analysis with DNN can detect different kinds of cancer [75; 76; 77; 78] and also extract information from an image that is difficult to detect by a human.
* Agriculture: Smart agriculture is a new approach to improve the productivity in agriculture [79] to provide food for an increasing population. Deep learning is applied in several agriculture areas [80]. These include fruit counting [81], identification and recognition of plants [82], land analysis and classification [83; 84], identification of diseases [85], and crop-type classification [86].
* Gaming: Game playing systems with deep learning achieve a playing level above human playing accuracy. Recent examples are chess [87] and go [88].
* Others: Several other areas are benefiting from deep learning: credit risk predictions [89; 90], solar power forecasting and analysis [91; 92], manufacturing [17], customer service [93], autonomous driving [10], digital marketing [94], and fraud detection [95].