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  2. Backpropagation - Wikipedia

    en.wikipedia.org/wiki/Backpropagation

    Backpropagation computes the gradient of a loss function with respect to the weights of the network for a single input–output example, and does so efficiently, computing the gradient one layer at a time, iterating backward from the last layer to avoid redundant calculations of intermediate terms in the chain rule; this can be derived through ...

  3. Delta rule - Wikipedia

    en.wikipedia.org/wiki/Delta_rule

    To find the right derivative, we again apply the chain rule, this time differentiating with respect to the total input to , : = () Note that the output of the j {\displaystyle j} th neuron, y j {\displaystyle y_{j}} , is just the neuron's activation function g {\displaystyle g} applied to the neuron's input h j {\displaystyle h_{j}} .

  4. Automatic differentiation - Wikipedia

    en.wikipedia.org/wiki/Automatic_differentiation

    Automatic differentiation is a subtle and central tool to automatize the simultaneous computation of the numerical values of arbitrarily complex functions and their derivatives with no need for the symbolic representation of the derivative, only the function rule or an algorithm thereof is required [3] [4]. Auto-differentiation is thus neither ...

  5. Feedforward neural network - Wikipedia

    en.wikipedia.org/wiki/Feedforward_neural_network

    This is an example of supervised ... The derivative to be calculated depends on the induced ... [26] Paul Werbos applied backpropagation to neural networks in ...

  6. Vanishing gradient problem - Wikipedia

    en.wikipedia.org/wiki/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. [1]

  7. Why Am I Snacking So Much? (& How to Stop) - AOL

    www.aol.com/why-am-snacking-much-stop-125800077.html

    For example, you could pour some popcorn into a bowl instead of bringing the whole bag to the sofa. You could also buy single-serve bags of chips instead of family-size bags. nortonrsx/istockphoto

  8. Backpropagation through time - Wikipedia

    en.wikipedia.org/wiki/Backpropagation_through_time

    Then, the backpropagation algorithm is used to find the gradient of the loss function with respect to all the network parameters. Consider an example of a neural network that contains a recurrent layer and a feedforward layer . There are different ways to define the training cost, but the aggregated cost is always the average of the costs of ...

  9. When It Comes to Weight Loss, These 2 Nutrients Can Help ...

    www.aol.com/comes-weight-loss-2-nutrients...

    When It Comes to Weight Loss, These 2 Nutrients Can Help ‘Significantly,’ Finds a New Study