CNN

CNN

Introduction

The Cable News Network (CNN) is a global news organization with its main office located in Atlanta, Georgia, in the United States.Currently owned by the Manhattan-based media conglomerate Warner Bros. Discovery (WBD), CNN was the first television network in the United States to offer 24-hour news coverage and the first all-news channel. It was founded in 1980 as a 24-hour cable news channel by American media proprietor Ted Turner and Reese Schonfeld.
In the US, there were 80 million television households that were CNN subscribers as of February 2023. With an average of 580,000 people daily, CNN ranked third in cable news network viewing in June 2021, behind Fox News and MSNBC, according to Nielsen. This was a 49% decrease from the previous year, as viewership fell sharply across all cable news networks. CNN started the year ranked 14th out of all basic cable networks, but in 2019 it shot up to 7th place during a major surge for the three largest cable news networks (breaking Fox News’s 5- and MSNBC’s 6-ranking streaks), then dropped back to number 11 in 2021 and then fell even further to number 21 in 2022.
Viewers in over 212 nations and territories have access to CNN foreign, which broadcasts CNN programming globally. However, from May 2019, the US local edition of CNN has absorbed foreign news coverage to save money on programming. The American version, often known as CNN (US), is accessible in Canada and on a few Caribbean islands. CNN also carries programming in India under the name CNN-News18, as well as in Japan, where it debuted on CNNj in 2003 and is simultaneously translated into Japanese.

CNN

History

On June 1, 1980, at 5:00 p.m. Eastern Time, the Cable News Network debuted. The channel’s debut newscast was anchored by David Walker and Lois Hart, a husband and wife duo, following an introduction by Ted Turner.The majority of CNN’s initial 200 workers were hired by executive vice president Burt Reinhardt, who also brought on Bernard Shaw as the network’s first news anchor.
Since its launch, CNN has extended its reach to include websites, specialty closed-circuit channels (like CNN Airport), and a number of cable and satellite television providers. In addition to more than 900 associated local stations (which also get news and features material via the video newswire service CNN Newsource), the corporation has 42 bureaus (12 domestic, 31 international), as well as multiple regional and foreign-language networks worldwide.The Turner Broadcasting System was eventually acquired by conglomerate Time Warner (later WarnerMedia) in 1996 after a merger with Discovery Inc. created Warner Bros. Discovery. This acquisition was made possible by the channel’s popularity, which elevated creator Ted Turner to the status of true tycoon.

CNN

About us

In more than 200 countries worldwide, CNN Worldwide offers more than two dozen news and information services via cable, satellite, radio, wireless devices, and the Internet.
More people are reached by CNN domestically than by any other TV news organization in the US on television, the internet, and mobile devices. With over 260 million households throughout the globe, CNN is the most widely broadcast news channel internationally. Additionally, the CNN Digital Network is constantly the top online destination for news and current events.
The world’s most watched 24-hour news network is CNN International. With a network of 38 satellites, CNN International is available to over 260 million television households across more than 200 countries and territories. CNN International has been divided into five distinct regions since September 1997: CNN International South Asia, CNN International Europe / Middle East / Africa, CNN International Asia Pacific, CNN International Latin America, and CNN International North America.
Furthermore, CNN New source is the most widely used news service globally, collaborating with hundreds of regional and global news outlets. Turner Broadcasting System, Inc., a Time Warner Company, is the parent company of CNN.

convolutional neural network (CNN)

CNN

Convolutional neural networks, often known as convnets or CNNs, are what?

One subset of machine learning is the convolutional neural network, sometimes known as a convnet or CNN. It is one of the many varieties of artificial neural networks that are employed for diverse kinds of data and applications. For deep learning techniques, a CNN is a type of network design that is particularly useful for applications involving the processing of pixel input, such as image recognition.
CNNs are the preferred network architecture for object identification and recognition in deep learning, while there are other varieties of neural networks as well. They are therefore ideal for computer vision (CV) jobs and critical object recognition applications like facial recognition and self-driving cars.

Within convolutional neural networks

Deep learning techniques contain artificial neural networks (ANNs) as a fundamental component. Recurrent neural networks (RNNs), which accept input in the form of sequential or time series data, are one kind of ANN. Applications involving speech recognition, image captioning, language translation, and natural language processing (NLP) can all benefit from it.
Another kind of neural network that may extract important information from picture and time series data is the CNN. This makes it extremely useful for applications involving images, like pattern identification, object classification, and picture recognition. A CNN uses matrix multiplication and other linear algebraic concepts to find patterns in an image. CNNs may also categorize signal and audio data.
The design of a CNN is comparable to the structure of connectivity found in the human brain. Similar to how the brain is made up of billions of neurons, each neuron in a CNN is structured differently. The arrangement of neurons in a CNN is actually similar to that of the frontal lobe of the brain, which processes visual stimuli. Because the full visual field is covered by this configuration, classic neural networks do not face the issue of piecemeal image processing, where images must be fed in reduced resolution segments. A CNN performs better when it comes to image inputs, speech or audio signal inputs, and other input types than the previous networks.

CNN

CNN tiers

A thorough education Convolutional, pooling, and fully connected (FC) layers make up CNN’s three layers. The FC layer comes last and is preceded by the convolutional layer.
The CNN gets more sophisticated as it moves from the convolutional layer to the FC layer. Because of its escalating intricacy, the CNN is able to recognize increasingly significant areas and intricate details of an image until ultimately identifying the item in its whole.
layer of convolution. The convolutional layer, the central component of a CNN, is where most computations take place. The first convolutional layer may be followed by a second one. A kernel or filter inside this layer traverses the image’s receptive fields during the convolution process to determine whether a feature is present.
The kernel covers the whole image throughout a number of iterations. A dot product between the filter and the input pixels is computed at the end of each iteration. Convolutional features, also referred to as feature maps, are the ultimate products of the dots series.Convolutional features, also referred to as feature maps, are the ultimate products of the dots series. In this layer, the image is ultimately transformed into numerical values so that the CNN can analyze the image and identify pertinent patterns in it.
Layer of pooling. The pooling layer applies a kernel or filter across the input image, just like the convolutional layer does. However, the pooling layer loses some information while reducing the number of parameters in the input, in contrast to the convolutional layer. Positively, this layer streamlines the CNN and increases its effectiveness.

Layer that is fully connected. Based on the features retrieved from the earlier levels, CNN classifies images in the FC layer. In this case, fully connected indicates that every activation unit or node in the subsequent layer is connected to every input or node from the previous layer.
Because doing so would create an unduly thick network, not every layer in the CNN is fully connected. Not only would it be computationally costly, but it would also result in higher losses and lower output quality.

How do neural networks with convolutions operate?

Each layer in a CNN can learn to identify distinct characteristics in an input image. Each image is subjected to a filter or kernel in order to provide an output that improves and becomes more detailed with each layer. The filters may begin as basic characteristics in the lower layers.
The filters are more complicated at each layer above in order to find and verify features that specifically reflect the input object. As a result, the input for the subsequent layer is the output of each convolved image, or the image that is partially recognized after each layer. The CNN identifies the picture or object it represents in the final layer, an FC layer.
The input image is passed through a series of these filters during convolution. Each filter performs its function and transfers its output to the filter in the following layer as it activates specific features from the image. As dozens, hundreds, or even thousands of layers are added, the processes are repeated as each layer gains the ability to recognize distinct features. Ultimately, the CNN is able to recognize the full object because to all of the picture data that has been processed through its many layers.

CNN

Neural networks vs CNNs

The primary issue with conventional neural networks (NNs) is their limited scalability. A standard neural network (NN) might yield acceptable results for smaller images with fewer color channels. However, when an image gets bigger and more complicated, more processing power and resources are required, which calls for a bigger, more expensive NN.
Furthermore, as time goes on, the overfitting issue also appears, in which the NN attempts to learn an excessive amount of detail from the training set. Additionally, it might learn from the noise in the data, which would impact how well it performs on test datasets. In the end, the NN is unable to recognize the object itself as well as the characteristics or patterns in the data set.
A CNN, on the other hand, uses parameter sharing. Every node in the CNN is connected to every other node in each layer. Each CNN has a weight as well; this is called parameter sharing; the weights stay fixed while the layers’ filters move over the image. Because of this, the entire CNN system uses less computing power than a NN system.

CNNs’ advantages for deep learning

Neural networks having three or more layers are used in deep learning, a subset of machine learning. A network with several layers can produce findings that are more accurate than one layer alone. Depending on the application, deep learning uses both RNNs and CNNs.
CNNs are very helpful for image identification, image classification, and computer vision (CV) applications because they yield incredibly accurate results, especially when a large amount of data is involved. As the object data passes through the CNN’s numerous layers, the CNN also picks up the item’s features through repeated repeats. The requirement for manual feature extraction, or feature engineering, is eliminated by this direct (and deep) learning.
CNNs can be retrained for new recognition tasks and built on preexisting networks. These advantages open up new opportunities to use CNNs for real-world applications without increasing computational complexities or costs.
As seen earlier, CNNs are more computationally efficient than regular NNs since they use parameter sharing. The models are easy to deploy and can run on any device, including smartphones.

Applications of convolutional neural networks

Convolutional neural networks are already used in a variety of CV and image recognition applications. Unlike simple image recognition applications, CV enables computing systems to also extract meaningful information from visual inputs (e.g., digital images) and then take appropriate action based on this information.
The most common applications of CV and CNNs are used in fields such as the following:
medical care. CNNs may search through thousands of visual reports to find any unusual health circumstances, like the presence of cancerous cells.
automobiles. Research on self-driving autos and autonomous vehicles is being powered by CNN technology.

CNN


social media platforms. CNNs are used by social media networks to recognize individuals in user photos and assist users in tagging their friends.
Shop. Visual search e-commerce platforms enable brands to suggest products that are likely to be of interest to a customer.
Police use of facial recognition technology. To create new photos that may be used to train deep learning models for facial recognition, generative adversarial networks, or GANs, are employed.
audio handling for virtual helpers. Virtual assistants’ CNNs pick up on and recognize terms that users speak, process the input to direct their actions, and reply to the user.

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