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  1. Vanishing gradient problem

    In machine learning, the vanishing gradient problem is encountered when training neural networks with gradient-based learning methods and backpropagation. In such methods, during each training iteration, each neural network weight receives an update proportional to the partial derivative of the loss function with respect to the current weight. The problem is that as the network depth or sequence length increases, the gradient magnitude typically is expected to decrease, slowing the training process. In the worst case, this may completely stop the neural network from further learning. As one example of this problem, traditional activation functions such as the hyperbolic tangent function have gradients in the range [-1,1], and backpropagation computes gradients using the chain rule. Wikipedia

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  2. Jun 12, 2023The vanishing gradient problem is mostly attributed to the choice of activation functions and optimization methods in DNNs. Vanishing gradient problems generally occurs when the value of partial ...
  3. kdnuggets.com

    Jun 15, 2023Learn how the sigmoid function causes the vanishing gradient problem in deep neural networks and how to overcome it using ReLU, leaky ReLU, or weight initialization. The blog post explains the forward and back propagation of neural networks and the derivatives of activation functions.
  4. machinelearningmastery.com

    Learn why vanishing gradient problem exists and how to visualize it in a neural network with Keras. See the difference between sigmoid and ReLU activation functions and how they affect the weights and gradients of each layer.
  5. machinelearningsite.com

    The vanishing gradient problem is caused by using inappropriate activation functions in deep networks. Consider an example in which you are building a classification model where the output is 0 or 1. Considering sigmoid is the suitable activation function in this case, you might set the activation function in every layer to sigmoid. ...
  6. analyticsvidhya.com

    Dec 6, 2024For z < 0, it takes on negative values, which allow the unit to have an average output closer to 0 thus alleviating the vanishing gradient problem. For z < 0, the gradients are non-zero. This avoids the dead neurons problem. For α = 1, the function is smooth everywhere. This speeds up the gradient descent since it does not bounce right and ...
  7. numerics.ovgu.de

    Introducing Vanishing and Exploding Gradients Vanishing Gradient Problem refers to the behaviour, when long term components go exponentially fast to norm 0, making it impossible for the model to learn correlations between events. Exploding Gradient Problem refers to the large increase in the norm of the gradient during training.

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