Recurrent Neural Networks, This is a follow-up to one of our previous posts, which you can read here if you missed it.
Let’s look into Recurrent Neural Networks and the different types of issues that they may handle. RNN is a deep learning technique that attempts to overcome the difficulty of modeling sequential data.
What exactly does “sequential data” imply?
The data is considered to be sequential when the points in a data set are dependent on the prior points.
RNN Use Cases
Let’s look at some of the RNN’s most common uses.
Take stock price data as an example; it is sequential, and the price of the stock tomorrow is determined by the price today. As a result, an RNN-based stock price forecast will be more appropriate.
Take, for example, a product review in any social media comment. RNN is also used for sentiment analysis. RNN can read the comments in order and provide the emotion (positive or negative).
The next word in a sentence can be predicted using an RNN. We’ve seen this technology in a variety of settings, including social media comments, SMS messages, and so on…
The word that is recommended in this scenario is very reliant on the previously inputted words and the context of the message.
Another interesting and increasingly popular application of RNN is speech to text. The identified voice in this situation is not solely based on the word sound RNN employs.
The content in the vicinity of such sound to recognize the words spoken into the device microphone.
Modeling of Language
Many exciting deep learning applications, including speech recognition, machine transition, and image processing, start with language modeling.
Language modeling is the technique of assigning a probability to a series of words in a straightforward fashion.
For example, a language model could look at a list of words and anticipate which one will come next. The context is formed by the sequence of words, with the most recent word serving as the input data.
You can generate both a predicted word and a new context including the input word using these two pieces of information. For this type of situation, recurrent neural networks are ideal.
The vectors (matrix vocabulary) in the RNN model are randomly initialized for all of the phrases that will be used for training.
The vector values are then modified during recurrent network training dependent on the context into which the word is being put.
As a result, words that are employed in comparable situations end up having similar vector space positions.
You now have a good understanding of what recurrent neural networks are and how they work.