Early stopping

Early stopping

  4:30 PM    mayuravaani

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 Early stopping can be implemented as a manual or automatic process.In manual early stopping, the training process is monitored, and the training is stopped when the validation accuracy reaches a satisfactory level or starts to decline.In automatic early stopping, a stopping criterion is defined based on some metrics (e.g., validation loss, validation accuracy) and the training process is stopped...

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Bias-variance Trade-off for reference

Bias-variance Trade-off for reference

  4:25 PM    mayuravaani

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 In the context of bias-variance trade-off, "bias" refers to the error that is introduced by approximating a real-world problem with a simpler model. This error is caused by making assumptions about the problem that may not be entirely accurate, and is often associated with underfitting. A model with high bias tends to be overly simplistic and may not capture all of the relevant information in...

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Bias and weight in neural networks - self reference

Bias and weight in neural networks - self reference

  4:20 PM    mayuravaani

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 While it is true that the weights and biases are initially assigned randomly, and the neural network uses backpropagation to adjust them based on the training data, it is still important to monitor the values of the weights and biases during training and ensure that they are within reasonable ranges.If the weights or biases become too large, the neural network can become unstable and start producing...

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Neural Network self references

Neural Network self references

  2:59 PM    mayuravaani

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  In a neural network, each neuron has a set of parameters associated with it, which typically include a weight for each input connection and a bias term.The weight parameter determines the strength of the connection between a neuron's input and its output, and is typically learned through a process called backpropagation during training. The bias term represents the neuron's inherent "activation...

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