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Artificial neural networks (ANNs) are models created using machine learning to perform a number of tasks.Their creation was inspired by biological neural circuitry. [1] [a] While some of the computational implementations ANNs relate to earlier discoveries in mathematics, the first implementation of ANNs was by psychologist Frank Rosenblatt, who developed the perceptron. [1]
In 2015, two techniques were developed to train very deep networks: the highway network was published in May 2015, [104] and the residual neural network (ResNet) in December 2015. [ 105 ] [ 106 ] ResNet behaves like an open-gated Highway Net.
In 1949, Donald Hebb described Hebbian learning, the idea that neural networks can change and learn over time by strengthening a synapse every time a signal travels along it. [8] Artificial neural networks were originally used to model biological neural networks starting in the 1930s under the approach of connectionism.
Support-vector machines (SVMs) and recurrent neural networks (RNNs) become popular. [3] The fields of computational complexity via neural networks and super-Turing computation started. [4] 2000s: Support-Vector Clustering [5] and other kernel methods [6] and unsupervised machine learning methods become widespread. [7] 2010s
They were the first to describe what later researchers would call a neural network. [69] The paper was influenced by Turing's paper ' On Computable Numbers ' from 1936 using similar two-state boolean 'neurons', but was the first to apply it to neuronal function. [ 60 ]
British mathematician John Conway invented the Game of Life in 1970. Basically, the Game of Life tracks the on or off state—the life—of a series of cells on a grid across timesteps ...
Although CNNs were invented in the 1980s, their breakthrough in the 2000s required fast implementations on graphics processing units (GPUs). In 2004, it was shown by K. S. Oh and K. Jung that standard neural networks can be greatly accelerated on GPUs. Their implementation was 20 times faster than an equivalent implementation on CPU. [59]
For many years, sequence modelling and generation was done by using plain recurrent neural networks (RNNs). A well-cited early example was the Elman network (1990). In theory, the information from one token can propagate arbitrarily far down the sequence, but in practice the vanishing-gradient problem leaves the model's state at the end of a long sentence without precise, extractable ...