The process of making predictions by applying a trained model to unlabeled examples . Inference has a somewhat different meaning in Statistics . See Inference (statistics) 1
목차 목차 관련 글 MatFormer: Nested Transformer for Elastic Inference “Foundation models are applied in a broad spectrum of settings with different inference constraints, from massiv…” Self-training (machine learning) “Use the model created in Step 1 to generate predictions (labels) on the unlabeled examples, moving those in whi…” Machine Learning Systems “Unlike resources that focus primarily on algorithms and model architectures, this book highlights the broader c…” 모델 (기계학습) “Within supervised machine learning, models differ somewhat.” Example (machine learning) “Unlabeled examples are used during inference.” ML crash course - Linear regression “Loss measures the distance between the model's predictions and the actual labels.” 인과추론 “Causal inference is the process of determining whether and how a cause leads to an effect, typically using stat…” Introduction to Machine Learning 관련 절: Definition of [[Machine learning|ML]] 기계학습 “2024-08-24 - What's Really Going On in Machine Learning?” How learning can guide evolution “Learning alters the shape of the search space in which evolution operates and thereby provides good evolutionar…” Semi-supervised learning “Semi-supervised learning can be useful if labels are expensive to obtain but unlabeled examples are plentiful.” Input layer 이 글을 인용: “That is, the input layer provides examples for training or inference.” 특성 (기계학습) “The following table shows three examples, each of which contains three features and one label:” Weight (machine learning) 이 글을 인용: “Training is the process of determining a model's ideal weights; inference is the process of using those learned…” 수렴 (기계학습) “In deep model, loss values sometimes stay constant or nearly so for many iterations before finally descending.” Generalization (machine learning) “You train a model on the examples in the training set.” Epoch (machine learning) “An epoch represents N/batch size training iterations, where N is the total number of examples.”