deep learning

Deep learning is an advanced type of machine learning that is used in applications such as computer vision, self-driving cars and natural language processing. For example, deep learning enables facial recognition to be more accurate, and it allows medical scans to be interpreted without human analysis.

The Layers
In the training phase of a deep learning model, thousands of images of similar objects, such as a car, truck, horse or human, are input as examples. Each image is divided into blocks of pixels, and the blocks are transformed into patterns by an algorithm known as a "convolutional filter." The result of each transformation is handed to another filter and so on, each layer synthesizing the previous and adding a finer interpretation of the original input. These are the so-called "hidden layers" between the input and output of a deep learning model.

The final result is a newly generated algorithm that becomes the "inference engine," which does the actual processing such as identifying an object or making a decision. The greater number of layers in the training phase, the more accurate the inference engine and the better the results. See DLA, AI, machine learning, GAN, neural network and TensorFlow.

单词	deep learning
释义	DictionarySeeneural network deep learning deep learning Deep learning is an advanced type of machine learning that is used in applications such as computer vision, self-driving cars and natural language processing. For example, deep learning enables facial recognition to be more accurate, and it allows medical scans to be interpreted without human analysis. The Layers In the training phase of a deep learning model, thousands of images of similar objects, such as a car, truck, horse or human, are input as examples. Each image is divided into blocks of pixels, and the blocks are transformed into patterns by an algorithm known as a "convolutional filter." The result of each transformation is handed to another filter and so on, each layer synthesizing the previous and adding a finer interpretation of the original input. These are the so-called "hidden layers" between the input and output of a deep learning model. The final result is a newly generated algorithm that becomes the "inference engine," which does the actual processing such as identifying an object or making a decision. The greater number of layers in the training phase, the more accurate the inference engine and the better the results. See DLA, AI, machine learning, GAN, neural network and TensorFlow. LegalSeeAIAcronymsSeede luxe
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