Dropout
Dropout is a regularization technique in AI, especially neural networks, that combats overfitting by randomly disabling neurons during training, promoting robust feature learning and improved generalization to new data.
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4 min read
Regularization
Browse all content tagged with Regularization
Overfitting
Overfitting is a critical concept in artificial intelligence (AI) and machine learning (ML), occurring when a model learns the training data too well, including noise, leading to poor generalization on new data. Learn how to identify and prevent overfitting with effective techniques.
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2 min read
Regularization
Regularization in artificial intelligence (AI) refers to a set of techniques used to prevent overfitting in machine learning models by introducing constraints during training, enabling better generalization to unseen data.
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9 min read
Underfitting
Underfitting occurs when a machine learning model is too simplistic to capture the underlying trends of the data it is trained on. This leads to poor performance both on unseen and training data, often due to lack of model complexity, insufficient training, or inadequate feature selection.
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5 min read
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