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nothig
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Which layer of the OSI model supplies services that allow user to interface with the network?
The Seven layers of the OSI model are: Application Presentation Session Transport Network Data-Link Physical I think the answer to your question is the Application layer.
What is the correct order for the OSI layers?
All people seem to need data processing, or Please do not through sausage pizza away Application, presentation, session, transport, network, data link, and physical. or Physical, data link, network, transport, session, presentation, and Application.
Difference between artificial neural network and conventional computer?
Parallel processingOne of the major advantages of the neural network is its ability to do many things at once. With traditional computers, processing is sequential--one task, then the next, then the next, and so on. The idea of threading makes it appear to the human user that many things are happening at one time. For instance, the Netscape throbber is shooting meteors at the same time that the page is loading. However, this is only an appearance; processes are not actually happening simultaneously.The artificial neural network is an inherently multiprocessor-friendly architecture. Without much modification, it goes beyond one or even two processors of the von Neumann architecture. The artificial neural network is designed from the onset to be parallel. Humans can listen to music at the same time they do their homework--at least, that's what we try to convince our parents in high school. With a massively parallel architecture, the neural network can accomplish a lot in less time. The tradeoff is that processors have to be specifically designed for the neural network.The ways in which they functionAnother fundamental difference between traditional computers and artificial neural networks is the way in which they function. While computers function logically with a set of rules and calculations, artificial neural networks can function via images, pictures, and concepts.Based upon the way they function, traditional computers have to learn by rules, while artificial neural networks learn by example, by doing something and then learning from it. Because of these fundamental differences, the applications to which we can tailor them are extremely different. We will explore some of the applications later in the presentation.Self-programmingThe "connections" or concepts learned by each type of architecture is different as well. The von Neumann computers are programmable by higher level languages like C or Java and then translating that down to the machine's assembly language. Because of their style of learning, artificial neural networks can, in essence, "program themselves." While the conventional computers must learn only by doing different sequences or steps in an algorithm, neural networks are continuously adaptable by truly altering their own programming. It could be said that conventional computers are limited by their parts, while neural networks can work to become more than the sum of their parts.SpeedThe speed of each computer is dependant upon different aspects of the processor. Von Neumann machines requires either big processors or the tedious, error-prone idea of parallel processors, while neural networks requires the use of multiple chips customly built for the application.
Workflow groupware and telepresence systems are examples of which network application?
Workflow, Groupware, and Telepresence Systems are examples of network collaboration applications.
What is a system that attempts to imitate the behavior of the human brain?
Neural Network: System that attempts to imitate the behavior of the human brain.-Straight outta Discovering Computers in 2009.
Advantage and disadvantage of neural network?
Advantages and disadvantages of Artificial Neural NetworkAdvantages:· A neural network can perform tasks that a linear program cannot.· When an element of the neural network fails, it can continue without any problem by their parallel nature.· A neural network learns and does not need to be reprogrammed.· It can be implemented in any application and without any problem.Disadvantages:· The neural network needs training to operate.· The architecture of a neural network is different from the architecture of microprocessors therefore needs to be emulated.· Requires high processing time for large neural networks.
What is epochs in neural network?
A step in the training process of an artificial neural network
What is application of oops?
A Mistake
What is momentum neural networks?
momentum neural network
What is the relationship between a neural network and a local area network?
It depends on the context and application. A neural network is a network fashioned after the brain. Where pathways are opened to trigger responses from multiple "data centers" in the brain, based on stimulus. A LAN is nothing like it, other than the similarity that it has a transmission medium. Yet a LAN is useless without a brain.
Disadvantage of artificial neural network?
the neural networks need training to operate. the architecture of a neural network is different from the architecture of microprocessor therefore needs to be emulated.
What is basic neutron of neural network?
Neural networks have nothing to do with neutrons.
What is iteration in neural network?
gudu gudu bamboo
How does the human brain learn?
By forming an neural network
Which structure is responsible for transducing light into neural impulses?
The retina is responsible for transducing light into neural impulses. It is a layer of tissue located at the back of the eye that contains photoreceptor cells (rods and cones) that convert light into electrical signals that can be processed by the brain.
Is 20Q a Neural Network?
20Q is a true neural network. The answers to questions stimulate target nodes (objects), which in turn stimulate the next question to ask. The "brain" is about as complex as an insect's brain.
What does the letters CNN mean?
A convolutional neural network (CNN) is a specific type of artificial neural network that uses perceptrons, a machine learning unit algorithm, for supervised learning, to analyze data. CNNs apply to image processing, natural language processing and other kinds of cognitive tasks. A convolutional neural network is also known as a ConvNet.
