Loss Functions Used In Artificial Intelligence

One of many important characteristics of the LSTM is that it addresses a key downside usually encountered in RNNs, their inability to analyse very long complex sequences. This is the well-known problem of the vanishing or exploding gradients and it emerges when derivatives throughout training grow to be ever so small or giant, harming the neural network’s learning efficacy. The issue was first addressed in 1994 by means of the LSTM and later with the Gated Recurrent Unit (GRU). I cannot go into details about the varied architectures and functionality or training processes of LSTM networks as there may be sufficient bibliography on the market for the curious mind. Within the core of the traditional RNN lies a typical Artificial Neural Network (ANN) layer applying some form of function, like e.g. the hyperbolic tangent, to the sum of its inputs which will or may not excite its neurons. Basically, a memory layer is formulated by choosing part of the output and feeding it back once more as input to the neural community. As a birds-eye view, the following graph exhibits the idea of an RNN.

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