A technique that normalizes the inputs to each layer in a neural network, making training faster and more stable.
A technique that normalizes the inputs to each layer in a neural network, making training faster and more stable. By keeping the distribution of layer inputs consistent, it helps prevent the internal shifts that slow down learning. Almost standard practice in modern deep networks.
A computing system loosely inspired by biological brains, consisting of interconnected nodes (neurons) organized in layers.
The process of teaching an AI model by exposing it to data and adjusting its parameters to minimize errors.
A technique that normalizes activations across the features of each training example, rather than across the batch.
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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