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Artificial Intelligence & Machine Learning vocabulary · Neural Networks and Deep Learning

Layer

Meaning in artificial intelligence & machine learning, with examples

/ˈleɪər/nounCore term

In artificial intelligence & machine learning, layer means: One level of a neural network: a group of units that takes the output of the previous level, transforms it and passes the result on to the next one.

In Turkish: katman

How it is used

Networks have an input layer, hidden layers and an output layer. Engineers add, remove, freeze or unfreeze layers, and the 'deep' in deep learning refers to having many of them. Common types include dense, convolutional and attention layers.

Example sentences

  1. We froze the first ten layers and trained only the top two.

  2. Adding another hidden layer didn't help; the model just overfit faster.

Common collocations

  • hidden layer
  • output layer
  • freeze layers
  • add a layer
  • dense layer
  • neural networkA model made of many connected units arranged in layers, loosely inspired by the brain. Each connection has a weight that is adjusted during training.
  • deep learningThe part of machine learning that uses neural networks with many layers. It powers modern speech recognition, image recognition and large language models.
  • activation functionA simple mathematical function applied to the output of each unit in a neural network. It adds non-linearity, which lets the network learn complex patterns.
  • weightsThe learned numbers on the connections of a neural network, which decide how strongly each input affects the next layer. Together they hold what the model has learned.

This word in other fields

The same English word can have a different meaning in another field:

More terms: Neural Networks and Deep Learning

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