Loss Function
[/lɔːs ˈfʌŋkʃən/]
nounpl: Loss Functions
Função de Perda
1. A mathematical function that measures the difference between predicted values and actual values in machine learning models, used to quantify how well or poorly a model is performing.
The mean squared error is a common loss function used in regression problems.
O erro quadrático médio é uma função de perda comum usada em problemas de regressão.
2. In optimization, the objective function that an algorithm attempts to minimize during the training process of a neural network or statistical model.
During backpropagation, the loss function guides the adjustment of model weights.
Durante a retropropagação, a função de perda guia o ajuste dos pesos do modelo.
3. A penalty function that quantifies the cost of prediction errors for each training example.
Cross-entropy loss function is widely used in classification tasks.
A função de perda de entropia cruzada é amplamente usada em tarefas de classificação.
Loss Function is a technical term predominantly used in machine learning, data science, and artificial intelligence communities in both Brazil and the USA. In Brazil, as the field of AI and machine learning has grown significantly in major tech hubs like São Paulo, the terminology is increasingly standardized. The term is used interchangeably with 'Função de Custo' (Cost Function) in many contexts, though 'Função de Perda' is the more precise technical term in Portuguese academic literature.
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