A regularization technique that randomly deactivates a percentage of neurons during training.
A regularization technique that randomly deactivates a percentage of neurons during training. Forces the network to not rely on any single neuron, making it more robust. Think of it as training many slightly different networks simultaneously. One of the simplest and most effective ways to prevent overfitting.
Techniques that prevent a model from overfitting by adding constraints during training.
When a model memorizes the training data so well that it performs poorly on new, unseen data.
A computing system loosely inspired by biological brains, consisting of interconnected nodes (neurons) organized in layers.
A mathematical function applied to a neuron's output that introduces non-linearity into the network.
An optimization algorithm that combines the best parts of two other methods — AdaGrad and RMSProp.
Artificial General Intelligence.
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