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Karteikarten

Neural Computation

Brenna Cantwell Englisch 12 Karten

Karten in diesem Stapel (12)

  1. 1ANNs are worth studying because they can be extremely powerful ________.computational devices
  2. 2ANNs can be very efficient due to their massive ______.parallelism
  3. 3ANNs are both _____ tolerant and _____ tolerant.fault tolerant and noise tolerant
  4. 4ANNs are useful for both the scientific goal of __________, and the engineering goal of ________.modelling how real brains work, building efficient systems for real-world applications
  5. 5_______ is the most realistic representation of individual spike timings for ANN neurons, but to generate efficient learning algorithms, we use an approximation called ______.spike time coding
    rate coding
  6. 6The simplest ANNs consist of a set of _______, represented in a formula by indices _, _, and __. Activation flows between them via synapses with strengths ____, and ____.McCulloch-Pitts neurons
    k, i, and j
    w(ki), and w(ij)
  7. 7The first step of building an ANN is to specify the problem in terms of _____, and required ______.inputs, outputs
  8. 8Before finding appropriate connection weights for an ANN, it is best to choose the _______ form of network that is able to solve the problem.simplest
  9. 9The arrangement of one input layer of activations feeding forward to one output layer of McCulloch-Pitts neurons is known as a simple ________.Perceptron
  10. 10The Perceptron Learning rule is an algorithm that adjusts network ______ to minimize the difference between _____ outputs and _______outputs.weights, actual, target
  11. 11We quantify the difference between target and actual outputs by defining an _______ function: Cross Entropy for classification, or Sum Squared Error for regression.error function
  12. 12—

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Karteikarten am Neural Computation, erstellt von Brenna Cantwell am 18/08/2016.
Erstellt von
Brenna Cantwell
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