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  2. Gradient descent - Wikipedia

    en.wikipedia.org/wiki/Gradient_descent

    Gradient descent is a method for unconstrained mathematical optimization. ... "Gradient Descent, How Neural Networks Learn". 3Blue1Brown. October 16, 2017 ...

  3. Backpropagation - Wikipedia

    en.wikipedia.org/wiki/Backpropagation

    In machine learning, backpropagation [1] is a gradient estimation method commonly used for training a neural network to compute its parameter updates.. It is an efficient application of the chain rule to neural networks.

  4. Stochastic gradient descent - Wikipedia

    en.wikipedia.org/wiki/Stochastic_gradient_descent

    Backpropagation was first described in 1986, with stochastic gradient descent being used to efficiently optimize parameters across neural networks with multiple hidden layers. Soon after, another improvement was developed: mini-batch gradient descent, where small batches of data are substituted for single samples.

  5. Delta rule - Wikipedia

    en.wikipedia.org/wiki/Delta_rule

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  6. Vanishing gradient problem - Wikipedia

    en.wikipedia.org/wiki/Vanishing_gradient_problem

    In machine learning, the vanishing gradient problem is the problem of greatly diverging gradient magnitudes between earlier and later layers encountered when training neural networks with backpropagation. In such methods, neural network weights are updated proportional to their partial derivative of the loss function. [1]

  7. Recurrent neural network - Wikipedia

    en.wikipedia.org/wiki/Recurrent_neural_network

    Recurrent neural networks ... Gradient descent is a first-order iterative optimization algorithm for finding the minimum of a function. In neural networks, ...

  8. Neural tangent kernel - Wikipedia

    en.wikipedia.org/wiki/Neural_tangent_kernel

    Here, the neural network is a scalar function trained on inputs drawn from the unit circle. The number of neurons in each layer is called the layer’s width. Consider taking the width of every hidden layer to infinity and training the neural network with gradient descent (with a suitably small learning rate).

  9. Early stopping - Wikipedia

    en.wikipedia.org/wiki/Early_stopping

    Gradient descent methods are first-order, iterative, optimization methods. Each iteration updates an approximate solution to the optimization problem by taking a step in the direction of the negative of the gradient of the objective function.