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  2. Mathematics of artificial neural networks - Wikipedia

    en.wikipedia.org/wiki/Mathematics_of_artificial...

    For each weight: Multiply the weight's output delta and input activation to find the gradient of the weight. Subtract the ratio (percentage) of the weight's gradient from the weight. The learning rate is the ratio (percentage) that influences the speed and quality of learning. The greater the ratio, the faster the neuron trains, but the lower ...

  3. Neural network (machine learning) - Wikipedia

    en.wikipedia.org/wiki/Neural_network_(machine...

    In machine learning, a neural network (also artificial neural network or neural net, abbreviated ANN or NN) is a model inspired by the structure and function of biological neural networks in animal brains. [1] [2] A neural network consists of connected units or nodes called artificial neurons, which loosely model the neurons in the brain ...

  4. Types of artificial neural networks - Wikipedia

    en.wikipedia.org/wiki/Types_of_artificial_neural...

    The Euclidean distance is computed from the new point to the center of each neuron, and a radial basis function (RBF, also called a kernel function) is applied to the distance to compute the weight (influence) for each neuron. The radial basis function is so named because the radius distance is the argument to the function. Weight = RBF(distance)

  5. Synaptic weight - Wikipedia

    en.wikipedia.org/wiki/Synaptic_weight

    The changes in synaptic weight that occur is known as synaptic plasticity, and the process behind long-term changes (long-term potentiation and depression) is still poorly understood. Hebb's original learning rule was originally applied to biological systems, but has had to undergo many modifications as a number of theoretical and experimental ...

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    Get AOL Mail for FREE! Manage your email like never before with travel, photo & document views. Personalize your inbox with themes & tabs. You've Got Mail!

  7. Learning rule - Wikipedia

    en.wikipedia.org/wiki/Learning_rule

    It is done by updating the weight and bias [broken anchor] levels of a network when it is simulated in a specific data environment. [1] A learning rule may accept existing conditions (weights and biases) of the network, and will compare the expected result and actual result of the network to give new and improved values for the weights and ...

  8. 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.

  9. The View's Ana Navarro Details Weight Loss 'Struggle' Amid ...

    www.aol.com/entertainment/views-ana-navarro...

    Celebrities' Weight Loss and Transformations: Before and After Pictures. Read article. Navarro is the latest celeb to chime in on the widespread debate about taking Ozempic — a drug intended for ...