backpropagation
Backpropagation is a mathematical algorithm used in artificial neural networks to train the network by adjusting the weights of its connections. It involves propagating the error from the output layer back through the layers of the network to update the individual weights, allowing the network to learn from its mistakes and improve its performance over time.
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Related Concepts (22)
- activation functions
- artificial neural networks
- backpropagation through time
- backward pass
- chain rule
- convergence
- deep learning
- dropout regularization
- error backpropagation
- error calculation
- feedforward neural networks
- forward pass
- fully connected layers
- gradient descent
- inference in neural networks
- loss functions
- mini-batch gradient descent
- overfitting
- recurrent neural networks
- stochastic gradient descent
- training algorithms
- weight update
Similar Concepts
- action potential propagation
- backpropagation through time (bptt)
- backward chaining
- backwards planning
- batch gradient descent
- convergence of gradient descent
- error propagation
- forward chaining
- inference and forward propagation
- multilayer perceptron
- multilayer perceptrons
- neural network modeling
- neural network training
- regression
- repolarization