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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 ...
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 ...
The propagation delay of a physical link can be calculated by dividing the distance (the length of the medium) in meter by its propagation speed in m/s. Propagation time = Distance / propagation speed. Example: Ethernet communication over a UTP copper cable with maximum distance of 100 meter between computer and switching node results in:
If that fails, resend the frame after either 0 s, 51.2 μs, 102.4 μs, or 153.6 μs. If that still fails, resend the frame after k · 51.2 μs, where k is a random integer between 0 and 2 3 − 1. For further failures, after the cth failed attempt, resend the frame after k · 51.2 μs, where k is a random integer between 0 and 2 c − 1.
Alternating bit protocol (ABP) is a simple network protocol operating at the data link layer (OSI layer 2) [citation needed] that retransmits lost or corrupted messages using FIFO semantics. It can be seen as a special case of a sliding window protocol where a simple timer restricts the order of messages to ensure receivers send messages in ...
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Java backporting tools are programs (usually written in Java) that convert Java classes bytecodes from one version of the Java Platform to an older one (for example Java 5.0 backported to 1.4). Java backporting tools comparison
A two-layer neural network capable of calculating XOR. The numbers within the neurons represent each neuron's explicit threshold. The numbers that annotate arrows represent the weight of the inputs. Note that If the threshold of 2 is met then a value of 1 is used for the weight multiplication to the next layer.