Layer normalization (LayerNorm) is a technique used in deep learning to normalize the distributions of intermediate layers. It was proposed by researchers Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E. Hinton.

• Natural Language Processing (NLP)
• Convolutional Neural Networks (CNN)
• Recurrent Neural Networks (RNN)
• Long Short-Term Memory Networks (LSTM)
• Transformers in Machine Learning
• Deep learning models training
• Generative Adversarial Networks (GANs)
• Speech Recognition

For example, in a neural network with multiple hidden layers, layer normalization can be applied to each hidden layer to ensure that the activation values have similar distributions across the layers. This can help improve the generalization of the model and reduce the risk of overfitting.

• Addressing AI risks