Suppose batch gradient descent in a deep network is taking excessively long to find a value of the parameters that achieves a small value for the cost function . Which of the following techniques could help find parameter values that attain a small value for J?

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  1. Suppose batch gradient descent in a deep network is taking excessively long to find a value of the parameters that achieves a small value for the cost function . Which of the following techniques could help find parameter values that attain a small value for J? (Check all that apply)
    •  Try mini-batch gradient descent
    •  Try initializing all the weights to zero
    •  Try better random initialization for the weights
    •  Try tuning the learning rate α
    •  Try using Adam

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Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization Coursera Quiz Answer

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  1. With the inverted dropout technique, at test time:
  2. Increasing the parameter keep_prob from (say) 0.5 to 0.6 will likely cause the following:
  3. Which of these is NOT a good learning rate decay scheme? Here, t is the epoch number
  4. These plots were generated with gradient descent; with gradient descent with momentum (β = 0.5) and gradient descent with momentum (β = 0.9). Which curve corresponds to which algorithm?

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