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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 ...
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 ...
Encog is a machine learning framework available for Java and .Net. [1] Encog supports different learning algorithms such as Bayesian Networks , Hidden Markov Models and Support Vector Machines . However, its main strength lies in its neural network algorithms.
This step is sometimes also called playout or rollout. A playout may be as simple as choosing uniform random moves until the game is decided (for example in chess, the game is won, lost, or drawn). Backpropagation: Use the result of the playout to update information in the nodes on the path from C to R. Step of Monte Carlo tree search.
Similar ideas have been used in feed-forward neural networks for unsupervised pre-training to structure a neural network, making it first learn generally useful feature detectors. Then the network is trained further by supervised backpropagation to classify labeled data.
In 1986, David E. Rumelhart et al. popularised backpropagation but did not cite the original work. [29] [8] In 2003, interest in backpropagation networks returned due to the successes of deep learning being applied to language modelling by Yoshua Bengio with co-authors. [30]
Persist (Java tool) Pointer (computer programming) Polymorphism (computer science) Population-based incremental learning; Prepared statement; Producer–consumer problem; Project Valhalla (Java language) Prototype pattern; Proxy pattern
Rprop, short for resilient backpropagation, is a learning heuristic for supervised learning in feedforward artificial neural networks. This is a first-order optimization algorithm. This algorithm was created by Martin Riedmiller and Heinrich Braun in 1992. [1]