Or through transformations over time in RNNs.įNNs with fixed topology have a problem-independent maximal Which are chains of possibly causal connections between subsequent unit activations, e.g.,įrom input units through hidden units to output units in feedforward NNs (FNNs) We consider the length of the corresponding credit assignment paths, To measure whether credit assignment in a given NN application RNNs can learn programs that mix sequentialĪnd parallel information processing in a natural and efficient way. The program of an RNN is its weight matrix. Unlike feedforward NNs, RNNs can implement while loops, recursion, etc. In fully connected RNNs, all units have connections to all non-input units. In a sense, sequence-processing recurrent NNs (RNNs) are the ultimate NNs,īecause they are general computers (an RNN can emulate the circuits of a microchip).
In a non-linear way) the aggregate activation of the network.ĭeep Learning in NNs is about accurately assigning credit across many such stages. May require long causal chains of computational stages, where each stage transforms (often Learning or credit assignment is about finding weights that make the NN exhibit desired behavior,ĭepending on the problem and how the units are connected, such behavior Some units may influence the environment by triggering actions. Other units through connections with real-valued weights from previously active units. Input units get activated through sensors perceiving the environment, LeCun et al (2015) provide a more limited view of more recent Deep Learning history.Ī standard NN consists of many simple, connected processors called units,Įach producing a sequence of real-valued activations. This article will focus on essential developments since the 1960s,Īddressing supervised, unsupervised, and (briefly) reinforcement learning. Which are much older though, dating back half a century. Subsequently it became especially popular in the context of deep NNs, The ancient term "Deep Learning" was first introducedĪnd to Artificial Neural Networks (NNs) by Aizenberg et al (2000). Potentially causal links between actions and consequences. It is about credit assignment in adaptive systems with long chains of Juergen Schmidhuber, Dalle Molle Institute for Artificial Intelligence, Manno-Lugano, Switzerlandĭeep Learning has revolutionised Pattern Recognition and Machine Learning.
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