Hyperparameters : Learning Rate, Regularization, Momentum, Sparsity, Deep Feedforward Networks

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  1. Learning Rate:
  • Learning rate is a hyperparameter that controls the step size in the optimization algorithm used to update the weights of a deep feedforward network.
  • A higher learning rate can lead to faster convergence, but may also result in overshooting and instability. A lower learning rate can help avoid these issues but may result in slower convergence.
  • The optimal learning rate depends on the specific problem being solved and the data being used.
  1. Regularization:
  • Regularization is a technique used to prevent overfitting in deep feedforward networks.
  • L1 and L2 regularization are commonly used techniques that add a penalty term to the loss function to encourage the weights to have smaller values.
  • Dropout regularization is another popular technique that randomly drops out some neurons during training to encourage the network to learn more robust representations.
  1. Momentum:
  • Momentum is a hyperparameter that controls the amount of “memory” used in the optimization algorithm to update the weights of a deep feedforward network.
  • Momentum helps to smooth out fluctuations in the optimization process and can help the network converge faster and more reliably.
  • A higher momentum can lead to faster convergence, but may also result in overshooting and instability. A lower momentum can help avoid these issues but may result in slower convergence.
  1. Sparsity:
  • Sparsity is a property of deep feedforward networks that refers to the degree to which the network has many weights that are close to zero.
  • Sparsity can help reduce the complexity of the network and improve its interpretability.
  • Techniques such as L1 regularization and pruning can be used to encourage sparsity in deep feedforward networks.
  1. Deep Feedforward Networks:
  • Deep feedforward networks are neural networks that have multiple layers between the input and output layers.
  • Deep networks are able to learn more complex representations of the input data and can achieve better performance on a wide range of tasks.
  • However, training deep networks can be challenging, and techniques such as pretraining, batch normalization, and residual connections have been developed to help overcome these challenges.

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