A state reached when loss values change very little or not at all with each iteration . A model converges when additional training won’t improve the model. In deep model , loss values sometimes stay constant or nearly so for many iterations before finally descending. During a long period of constant loss values, you may temporarily get a false sense of convergence.1
See also early stopping .
목차 목차 관련 글 ML crash course - Linear regression 이 글을 인용: “When training a model, you'll often look at a loss curve to determine if the model has converged.” Epoch (machine learning) 이 글을 인용: “In many cases, you'll need to experiment with how many epochs it takes for the model to converge.” Training loss 이 글을 인용: “A flat slope implies that the model has reached convergence.” Learning rate 이 글을 인용: “If you set the learning rate too high, gradient descent often has trouble reaching convergence.” 경사하강법 이 글을 인용: “Return to step one and repeat the process until it converges” Introduction to Machine Learning 관련 절: Definition of [[Machine learning|ML]] 모델 (기계학습) “Within supervised machine learning, models differ somewhat.” mini-batch “Therefore, each iteration determines the loss on a random 20 of the 1,000 examples and then adjusts the weights…” Machine Learning Systems “Machine Learning Systems provides a systematic framework for understanding and engineering machine learning (ML…” Self-training (machine learning) “A variant of self-supervised learning that is particularly useful when all of the following conditions are true:” Clipping (machine learning) “Gradient clipping forces gradient values within a designated range during training.” 기계학습 “2024-08-24 - What's Really Going On in Machine Learning?” 분류 모델 이 글을 인용: “Use as a rough indicator of model training progress/convergence for balanced datasets.” Loss curve 이 글을 인용: “A plot of loss as a function of the number of training iterations.” Generalization (machine learning) “To encourage generalization, regularization helps a model train less exactly to the peculiarities of the data i…” Downsampling (machine learning) “Downsampling helps balance the amount of training on the majority and minority classes.” 지도 학습 “Supervised machine learning is analogous to learning a subject by studying a set of questions and their corresp…” 배치 크기 “For instance, if the batch size is 100, then the model processes 100 examples per iteration.” 특성 (기계학습) “For instance, suppose you are training a model to determine the influence of weather conditions on student test…” Hyperparameter “You could set the learning rate to 0.01 before one training session.”