For example, the first hidden layer could learn how to detect edges, and the last learns how to detect more complex shapes specifically catered to the shape of the object we are trying to recognize. Every hidden layer increases the complexity of the learned image features. For example, home assistance devices that respond to your voice and know your preferences are powered by deep learning applications.ĬNNs learn to detect different features of an image using tens or hundreds of hidden layers. Industrial Automation: Deep learning is helping to improve worker safety around heavy machinery by automatically detecting when people or objects are within an unsafe distance of machines.Įlectronics: Deep learning is being used in automated hearing and speech translation. Teams at UCLA built an advanced microscope that yields a high-dimensional data set used to train a deep learning application to accurately identify cancer cells. Medical Research: Cancer researchers are using deep learning to automatically detect cancer cells. In addition, deep learning is used to detect pedestrians, which helps decrease accidents.Īerospace and Defense: Deep learning is used to identify objects from satellites that locate areas of interest, and identify safe or unsafe zones for troops.
When combined with clusters or cloud computing, this enables development teams to reduce training time for a deep learning network from weeks to hours or less.ĭeep learning applications are used in industries from automated driving to medical devices.Īutomated Driving: Automotive researchers are using deep learning to automatically detect objects such as stop signs and traffic lights. High-performance GPUs have a parallel architecture that is efficient for deep learning.
Deep learning requires large amounts of labeled data.While deep learning was first theorized in the 1980s, there are two main reasons it has only recently become useful: Recent advances in deep learning have improved to the point where deep learning outperforms humans in some tasks like classifying objects in images. This helps consumer electronics meet user expectations, and it is crucial for safety-critical applications like driverless cars. Deep learning achieves recognition accuracy at higher levels than ever before. How does deep learning attain such impressive results?
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