deep learning

Deep learning is an advanced type of machine learning architecture employed by neural networks, most commonly by "convolutional neural networks." Deep learning is used in applications such as computer vision, self-driving cars, natural language processing and online advertising. 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 being, are input as examples. The image is divided into pixels that are connected to several layers, each layer identifying a different block of pixels. By the time the image reaches the final layer, the input pattern has been identified. These are the so-called "hidden layers" between the input and output of a deep learning model. See convolutional neural network.

The deep learning phase turns the neural network into 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.

Algorithms Make Mistakes

Deep learning algorithms can be fooled. A famous example is mistaking a muffin for a puppy. Like a CAPTCHA, this verification test is used to ensure the viewer is human and not a bot, and images such as these can confuse an algorithm (see CAPTCHA).

Deep Learning in the Hierarchy

Deep learning is a subset within machine learning, which is a major category of artificial intelligence (AI).

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References in periodicals archive

This revolutionary work is motivated due to lacking transparency of the deep learning technology.

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An honest machine learning scientist must reserve confidence in their deep learning networks, because there is no consensus on how or why the deep algorithms obtain the performance that they do.

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For this research project, Long and Corns will use data from the Meramec and Missouri basins to train a deep learning neural network to determine how deep and how quickly floodwaters will rise.

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Fujitsu Laboratories will endeavor to further increase the speed of deep learning, aiming to implement practical applications of this newly developed technology for Fujitsu's servers and supercomputers.

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Section 2 reviews the related applications of deep learning. In Section 3, we address the common models of deep learning.

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On page 24, Dr Ben Mills shares work his group at the Optoelectronic Research Centre is doing to explore how deep learning can improve the repeatability, precision, and control of femtosecond laser machining, with remarkable results achieved so far.

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A new approach is needed, and recent advancements in deep learning make it the ideal technology to address the velocity and volume of attacks.

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Baidu Institute of Technology chose to develop a certification for the high-growth artificial intelligence industry since deep learning technology is a necessary skill for technology practitioners and project managers.

New deep learning exams in China