multilayer perceptron

USAGE SUMMARY

The term "multilayer perceptron" is correct and usable in written English.
You could use it when discussing the architecture of neural networks. For example, "The multilayer perceptron is a basic building block of deep learning architectures."

✓ Grammatically correct

Science

Human-verified examples from authoritative sources

Exact Expressions

60 human-written examples

Multilayer perceptron.

EURASIP Journal on Image and Video Processing

In particular, we use a multilayer perceptron.

Engineering Applications of Artificial Intelligence

Particularly a multilayer perceptron, is used.

IFAC Proceedings Volumes

Open image in new window Figure 2 Multilayer perceptron.

International Journal of Mechanical and Materials Engineering

In particular, a multilayer perceptron (MLP) network [13] is used.

EURASIP Journal on Wireless Communications and Networking

For this purpose, a multilayer perceptron (MLP) is used.

EURASIP Journal on Advances in Signal Processing

The face recognition was ensured by a multilayer perceptron architecture.

EURASIP Journal on Information Security

The classification is achieved by a multilayer perceptron.

EURASIP Journal on Information Security

We used a multilayer perceptron with sigmoid activation functions.

Nanoscale Research Letters

Therefore, different multilayer perceptron and MNN were compared.

Journal of Petroleum Science and Engineering

The systems are modelled using a multilayer perceptron networks.

IFAC Proceedings Volumes

Expert writing Tips

Best practice

When describing a "multilayer perceptron", specify its architecture (number of layers, neurons per layer) and activation functions to provide a clear understanding of its configuration.

Common error

Avoid using "multilayer perceptron" as a blanket term for all neural networks. It's a specific type of feedforward network, and other architectures like convolutional neural networks or recurrent neural networks have distinct properties and applications.

Linguistic Context

The phrase "multilayer perceptron" functions primarily as a noun, specifically a technical term within the field of computer science and artificial intelligence. Ludwig AI indicates that is commonly used in academic and scientific contexts.

Expression frequency: Very common

Frequent in

Science

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Formal & Business

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Ludwig's WRAP-UP

The term "multilayer perceptron" is a grammatically sound and very common term in the scientific field, as Ludwig AI confirms. It refers to a specific type of feedforward artificial neural network. When using this term, it's crucial to provide sufficient detail about the network's architecture. Alternatives such as "feedforward neural network" or "artificial neural network" offer broader descriptions, while "deep neural network" specifies a network with numerous layers. Avoid generalizing "multilayer perceptron" to encompass all neural network types. By understanding its specific context and usage, you can effectively communicate about this fundamental concept in machine learning.

More alternative expressions

Phrases that express similar concepts, ordered by semantic similarity:

multi-layer neural network

Slightly different wording emphasizing the multiple layers.

MLP network

Acronym for Multilayer Perceptron, which is also a widely used abbreviation.

feedforward neural network

Broader term that encompasses multilayer perceptrons as a specific type.

artificial neural network

General term for networks mimicking the brain's structure; multilayer perceptron is a specific implementation.

deep neural network

Multilayer perceptrons with multiple hidden layers; emphasizes depth of network.

backpropagation network

Highlights the training algorithm commonly used with multilayer perceptrons.

fully connected network

Describes the connectivity pattern within a multilayer perceptron.

perceptron network

Focuses on the basic unit (perceptron) that makes up the network.

neural network classifier

Describes the function of a multilayer perceptron when used for classification tasks.

nonlinear classifier

Highlights the ability of multilayer perceptrons to model nonlinear relationships.

FAQs

How is a "multilayer perceptron" used in machine learning?

A "multilayer perceptron" is commonly used as a classifier or regressor in machine learning tasks. It learns complex patterns from data through a process called backpropagation, adjusting the weights of connections between neurons to minimize errors.

What are the key components of a "multilayer perceptron"?

The key components include an input layer, one or more hidden layers, and an output layer. Each layer consists of neurons connected by weighted connections. Activation functions introduce non-linearity, enabling the network to learn complex relationships.

What is the difference between a perceptron and a "multilayer perceptron"?

A perceptron is a single-layer neural network, while a "multilayer perceptron" has multiple layers (at least one hidden layer) between the input and output layers. This allows a "multilayer perceptron" to learn more complex, non-linear patterns than a single perceptron.

When should I use a "multilayer perceptron" versus other neural network architectures?

Use a "multilayer perceptron" when dealing with tabular data or when a simple, feedforward architecture is sufficient. For image or sequence data, consider convolutional neural networks or recurrent neural networks, respectively, as they are often better suited for those tasks.