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2. Backpropagation - Wikipedia

   en.wikipedia.org/wiki/Backpropagation

   This is the reason why backpropagation requires that the activation function be differentiable. (Nevertheless, the ReLU activation function, which is non-differentiable at 0, has become quite popular, e.g. in AlexNet) The first factor is straightforward to evaluate if the neuron is in the output layer, because then = and

3. Backdoor (computing) - Wikipedia

   en.wikipedia.org/wiki/Backdoor_(computing)

   A backdoor is a typically covert method of bypassing normal authentication or encryption in a computer, product, embedded device (e.g. a home router), or its embodiment (e.g. part of a cryptosystem, algorithm, chipset, or even a "homunculus computer"—a tiny computer-within-a-computer such as that found in Intel's AMT technology).

4. Defensive programming - Wikipedia

   en.wikipedia.org/wiki/Defensive_programming

   Secure programming is the subset of defensive programming concerned with computer security. Security is the concern, not necessarily safety or availability (the software may be allowed to fail in certain ways). As with all kinds of defensive programming, avoiding bugs is a primary objective; however, the motivation is not as much to reduce the ...

5. Backpropagation through time - Wikipedia

   en.wikipedia.org/wiki/Backpropagation_through_time

   Back_Propagation_Through_Time(a, y) // a[t] is the input at time t. y[t] is the output Unfold the network to contain k instances of f do until stopping criterion is met: x := the zero-magnitude vector // x is the current context for t from 0 to n − k do // t is time. n is the length of the training sequence Set the network inputs to x, a[t ...

6. NETtalk (artificial neural network) - Wikipedia

   en.wikipedia.org/wiki/NETtalk_(artificial_neural...

   Training NETtalk became a benchmark to test for the efficiency of backpropagation programs. For example, an implementation on Connection Machine-1 (with 16384 processors) ran at 52x speedup. An implementation on a 10-cell Warp ran at 340x speedup. [6] [7] The following table compiles the benchmark scores as of 1988.

7. Vanishing gradient problem - Wikipedia

   en.wikipedia.org/wiki/Vanishing_gradient_problem

   Hardware advances have meant that from 1991 to 2015, computer power (especially as delivered by GPUs) has increased around a million-fold, making standard backpropagation feasible for networks several layers deeper than when the vanishing gradient problem was recognized.