Comparing recurrent neural networks with similar models

In recent years, RNNs, similarly to any neural network model, have become widely popular due to the easier access to large amounts of structured data and increases in computational power. But researchers have been solving sequence-based problems for decades with the help of other methods, such as the Hidden Markov Model. We will briefly compare this technique to an RNNs and outline the benefits of both approaches.

The Hidden Markov Model (HMM) is a probabilistic sequence model that aims to assign a label (class) to each element in a sequence. HMM computes the probability for each possible sequence and picks the most likely one.

Both the HMM and RNN are powerful models that yield phenomenal results but, depending on the use case and resources available, RNN can be much more effective.