Recurrent neural network

The following are the pros and cons of a recurrent neural network when solving sequence-related tasks:

• Pros: Performs significantly better and is less expensive when working on complex tasks with large amounts of data.
• Cons: Complex to build the right architecture suitable for a specific problem. Does not yield better results if the prepared data is relatively small.

As a result of our observations, we can state that RNNs are slowly replacing HMMs in the majority of real-life applications. One ought to be aware of both models, but with the right architecture and data, RNNs often end up being the better choice.

Nevertheless, if you are interested in learning more about hidden Markov models, I strongly recommend going through some video series (https://www.youtube.com/watch?v=TPRoLreU9lA) and papers of example applications, such as Introduction to Hidden Markov Models by Degirmenci (Harvard University) (https://scholar.harvard.edu/files/adegirmenci/files/hmm_adegirmenci_2014.pdf) or Issues and Limitations of HMM in Speech Processing: A Survey (https://pdfs.semanticscholar.org/8463/dfee2b46fa813069029149e8e80cec95659f.pdf).