CNN Full Form is Cable News Network. Cable News Network is a cable news station in the United States launched by Ted Turner in 1980. CNN was the first 24-hour cable news channel in the United States when it initially debuted.
History of CNN
CNN debuted on June 1, 1980, after four years in development, with a newscast hosted by husband-and-wife duo Dave Walker and Lois Hart. The network won the competition in 1986 with its live coverage of the Challenger space shuttle accident. Five years later, CNN outperformed the other networks once more with its live “in-country” coverage of the Persian Gulf War. CNN, along with the rest of the Turner Broadcasting System, was acquired by Time Warner Inc. (later renamed WarnerMedia) in 1996.
Other Full Form of CNN
Another full form of CNN is Convolutional Neural Network. A Convolutional Neural Network is a form of deep learning algorithm that excels at picture detection and processing. It has several layers, including convolutional layers, pooling layers, and fully linked layers.
Features of CNN
- A CNN, or deep learning neural network, is a deep learning neural network designed for processing organised arrays of data, such as images.
- CNNs are highly good at detecting design in input images, such as lines, gradients, circles, or even eyes and faces.
- This is what makes convolutional neural networks so effective for computer vision.
- CNN does not require any preprocessing and can run straight on an underdone image.
- A convolutional neural network is a feed-forward neural network that rarely has more than 20 layers.
- The strength of a convolutional neural network is derived from a type of layer known as the convolutional layer.
- CNN is made up of multiple convolutional layers stacked on top of each other, each capable of recognising more complex structures.
- Handwritten digits can be recognised with three or four convolutional layers, while human faces can be differentiated with 25 layers.
- The goal of this field is to train machines to perceive the world in the same way that humans do, and to use that knowledge for a variety of tasks such as image and video recognition, image inspection and classification, media recreation, recommendation systems, natural language processing, and so on.
Types of CNN Models
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