Deep Machine Learning: Learn Artificial Intelligence, Machine Algorithms using Advanced Deep Machine Learning Techniques and Methods by Grant Joe

Author:Grant, Joe [Grant, Joe]
Language: eng
Format: epub
Published: 2020-09-08T16:00:00+00:00

Deep Learning Model Examples

Deep learning models are deployed in automated driving and medical devices. Automotive researchers use these models for the detection of different objects such as traffic lights and lampposts that are installed along the side of roads. These models are being used in driverless cars to detect pedestrians and discriminate them from moving cars and still objects. That’s how developers expect to lower the number of accidents. These models are also used in defense and aerospace to detect objects and discriminate them from satellites. That’s how experts can declare different zones as safe or unsafe.

These models also are being used in the medical field. Cancer researchers are using these systems to detect cancer cells in the human body. Deep learning models are also used to improve the safety of workers as they automatically detect when objects and people come close to machines and are likely to fall into danger. This technology is also being used for translation of speech and automated hearing.

Deep learning models work on the back of neural network architectures, which is why these models are often labeled as deep neural networks. The ‘deep’ in deep learning means the total number of layers in each neural network. Traditionally, a neural network only had two to three layers while deep neural networks may contain as many as 150 layers. Once you have created these models, you will have to train them with the help of neural network architectures and labeled data.

Deep learning has brought remarkable achievements for human beings and these achievements are visible in the form of automated cars. However, some highly awaited applications such as human-level translation, human-level dialogue systems, and human-level language understanding are largely elusive. However, AI has been through cycles of expectations and disappointments in the past.

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