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- Gradient Boosted Trees Description: Gradient boosting trees are an ensemble learning technique that builds models in a staged manner using decision trees as base(...) Read more
- Gradient-Based Optimization Description: Gradient-based optimization is an optimization method that uses the gradient of the objective function to find the minimum or(...) Read more
- Gaussian Naive Bayes Description: Gaussian naive Bayes is a variant of the naive Bayes algorithm used in machine learning and statistics. This model assumes that(...) Read more
- Gravitational Search Algorithm Description: The Gravitational Search Algorithm is a nature-inspired optimization method based on the law of gravity and mass interactions. This(...) Read more
- Gaussian Description: The Gaussian distribution, also known as the normal distribution, is a probability function that describes how the values of a(...) Read more
- Generative Adversarial Network Description: Generative Adversarial Networks (GANs) are a machine learning framework consisting of two neural networks that compete against each(...) Read more
- Group Normalization Description: Group normalization is a normalization technique used in neural networks that divides channels into groups, allowing each group to(...) Read more
- Gradient Flow Description: Gradient flow is a fundamental method in training neural networks, based on the propagation of gradients through the network to(...) Read more
- Gaussian Noise Description: Gaussian noise is a type of statistical noise characterized by having a probability density function that follows the normal(...) Read more
- Gradient Penalty Description: Gradient penalty is a regularization technique used in the field of machine learning and model optimization. Its main goal is to(...) Read more
- Global Average Pooling Description: Global Average Pooling (GAP) is an operation used in Convolutional Neural Networks (CNNs) that transforms the output of each(...) Read more
- Gradient-Based Learning Description: Gradient-Based Learning is a fundamental approach in training machine learning models, especially in neural networks and recurrent(...) Read more
- Gradient Ascent Description: Gradient Ascent is a fundamental optimization algorithm in the field of supervised learning and machine learning. Its main goal is(...) Read more
- Group Lasso Description: Group Lasso is a regularization technique that extends the traditional Lasso method, designed to handle sets of variables that are(...) Read more
- Group Decision Tree Description: The Group Decision Tree is a supervised learning model used to make decisions based on multiple criteria, considering the(...) Read more
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