A metric representing a model ’s loss during a particular training iteration . For example, suppose the loss function is MSE . Perhaps the training loss (the MSE) for the 10th iteration is 2.2, and the training loss for the 100th iteration is 1.9.1
A loss curve plots training loss versus the number of iterations. A loss curve provides the following hints about training:1
A downward slope implies that the model is improving.
An upward slope implies that the model is getting worse.
A flat slope implies that the model has reached convergence .
목차 목차 관련 글 ML crash course - Linear regression “When training a model, you'll often look at a loss curve to determine if the model has converged.” Test loss 이 글을 인용: “That's because a low test loss is a stronger quality signal than a low training loss or low validation loss.” Test set 이 글을 인용: “The training set and validation set are both closely tied to training a model.” mini-batch “For example, suppose the entire training set (the full batch) consists of 1,000 examples.” ML crash course - Logistic regression 관련 절: Loss and regularization Self-training (machine learning) “Self-training works by iterating over the following two steps until the model stops improving:” 역전파 “During the backward pass (backpropagation), the system reduces loss by adjusting the weights of all the neurons…” Log loss “Squared loss works well for a linear regression where the rate of change of the output values is constant.” Epoch (machine learning) “An epoch represents N/batch size training iterations, where N is the total number of examples.” L1 loss “L1 loss is less sensitive to outliers than L2 loss.” L2 loss “Mean squared error is the average L2 loss per example.” Generalization curve 이 글을 인용: “A plot of both training loss and validation loss as a function of the number of iterations.” Introduction to Machine Learning 관련 절: Definition of [[Machine learning|ML]] Early stopping “A method for regularization that involves ending training before training loss finishes decreasing.” Seeing again: treatment of functional visual loss “There is very little published literature on treatment strategies for functional visual loss.” Generalization (machine learning) “To encourage generalization, regularization helps a model train less exactly to the peculiarities of the data i…” Downsampling (machine learning) “For example, before training an image recognition model, downsampling high-resolution images to a lower-resolut…” 경사하강법 “A mathematical technique to minimize loss.” Regularization (machine learning) 관련 절: Training loss vs. real-world performance 배치 크기 “For instance, if the batch size is 100, then the model processes 100 examples per iteration.”