Label Smoothing
Label Smoothing is a regularization technique used in machine learning to make model predictions more robust by softening the target labels.
LaMDA
LaMDA is a conversational AI model developed by Google designed to generate more natural and engaging dialogue.
Layer Normalization
Layer normalization is a technique used in neural networks to stabilize and accelerate the training process by normalizing the inputs across the features of each layer.
Leaky ReLU
Leaky ReLU is a type of activation function used in artificial neural networks that allows a small, non-zero gradient when the unit is not active.
LeNet
LeNet is a pioneering convolutional neural network (CNN) architecture developed for image classification tasks.
Listen Attend and Spell
Listen, Attend and Spell Short Definition: Listen, Attend and Spell is a neural network model designed for automatic speech recognition tasks.
Log-Cosh Loss
Log-Cosh Loss is a smooth loss function used in regression tasks to measure the difference between predicted and actual values.
LogSoftmax
LogSoftmax is a mathematical function used in machine learning that applies the log function to the softmax output for numerical stability.
Long Short-Term Memory
Long Short-Term Memory (LSTM) is a type of recurrent neural network (RNN) architecture designed to effectively capture and learn long-range dependencies in sequential data.
Loss Function
A loss function is a mathematical formula that measures how far an AI model’s predictions are from the correct answers.
Lovasz Loss
Lovasz Loss is a surrogate loss function designed for optimizing the intersection-over-union (IoU) metric in machine learning models, particularly for image segmentation tasks.