inference and forward propagation
Inference and forward propagation are related concepts in the field of machine learning and artificial intelligence. Inference refers to the process of using a trained model to make predictions or draw conclusions about unseen or new data points based on the patterns learned from the training data. It involves applying statistical and mathematical techniques to compute the output or outcome of the model given the input. Forward propagation, on the other hand, is a specific technique used to calculate the output of a neural network model during the inference phase. It involves passing the input data through the network's layers in a forward direction, with each layer applying mathematical operations and transformations to generate the output of the model. So, in summary, inference is the broader concept of making predictions or drawing conclusions using a trained model, while forward propagation is a specific technique used in neural networks to compute the output during the inference process.
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