Margaret Rouse is an award-winning technical writer and teacher known for her ability to explain complex technical subjects simply to a non-technical, business audience. Over…
Network topology refers to the physical and logical arrangement of nodes and connections in a computing network.
Physical topology describes the layout of devices and cables, and logical topology describes the way in which data is transmitted within the network — regardless of the physical layout.
Physical and logical topologies play an important role in the overall performance, scalability and security of a network. Each topology has its own advantages and disadvantages, and the choice of topology depends on the specific requirements of the network.
Physical network topologies can be categorized into five basic models:
Network topologies used in artificial intelligence (AI) and machine learning (ML) include:
Feedforward neural networks: These networks consist of an input layer, one or more hidden layers and an output layer. Data is passed through the network in one direction, from input to output, and there are no feedback loops.
Convolutional neural networks (CNNs): These networks are commonly used for image recognition and classification tasks. They use convolutional layers to extract features from input data and pooling layers to reduce the size of the input data.
Recurrent neural networks (RNNs): These networks are commonly used for sequential data, such as text or speech. They have feedback loops that allow the network to use previous predictions as input for subsequent predictions.
Long short-term memory (LSTM) networks: These are a type of RNN that can maintain a long-term memory of previous inputs.
Autoencoders: These networks are used for unsupervised learning and data compression. They consist of an encoder network that compresses input data into a smaller representation, and a decoder network that reconstructs the original data from the compressed representation.
Generative adversarial networks (GANs): These networks are used for generating new data, such as images or text. They consist of two networks: a generator network that generates new data and a discriminator network that attempts to distinguish the generated data from real data.
Transformers: These networks use self-attention mechanisms to selectively focus on different parts of the input data when making predictions. They are commonly used for natural language processing tasks such as language translation and text classification.
In the most modern systems, networks have become so complex that traditional topologies now apply in different ways. One of these phenomena is the use of opaque systems to foil hackers or outside cyberattacks. Some experts are now suggesting that by shielding the IP addresses and isolating different parts of the network into segments, companies can practice better cybersecurity hygiene. All of that continues to change how network topologies are used.
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Margaret is an award-winning technical writer and teacher known for her ability to explain complex technical subjects to a non-technical business audience. Over the past twenty years, her IT definitions have been published by Que in an encyclopedia of technology terms and cited in articles by the New York Times, Time Magazine, USA Today, ZDNet, PC Magazine, and Discovery Magazine. She joined Techopedia in 2011. Margaret's idea of a fun day is helping IT and business professionals learn to speak each other’s highly specialized languages.
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