The human mind has different mechanisms for processing individual pieces of information and sequences.
For instance, we have a definition of the word like.
Consider how you would fill in the blanks in the following two sentences:
Would you like … coffee?
Would you like … a walk?
We see sequences everywhere.
Videos are sequences of images, audio files are sequences of sound samples, music is sequences of notes.
In all cases, there is a temporal dependency between the individual members of the sequence.
Changing the order of frames in a video will render it meaningless.
Changing the order of words in a sentence or article can completely change its meaning.
But they were not suitable for variable-length, sequential data.
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In feedforward networks, information moves in one direction.
In feedforward neural networks, information moves in one direction.
Therefore, feedforward networks know nothing about sequences and temporal dependency between inputs.
In recurrent neural networks, the output of hidden layers are fed back into the web link.
For instance, an image-captioning system takes a single image and outputs a description.
In the one-to-many RNN, a single output is mapped to a sequence.
The many-to-one mode is used when an input sequence is mapped onto a single output.
For instance, a machine translation RNN can take an English sentence as input and produce the French equivalent.
Many-to-many RNNs generate sequences from sequences.
you might also use RNNs to detect and filter out spam messages.
Chatbots are another prime program for recurrent neural networks.
This is an example of the many-to-many RNN mode.
Another use for recurrent neural networks that is related to natural language is speech recognition and transcription.
RNNs can be trained to convert speech audio to text or vice versa.
But the use of recurrent neural networks is not limited to text and language processing.
RNNs can be applied to any bang out of sequential data.
RNNs are also useful in time series prediction.
Beyond recurrent neural networks
The original RNNs suffered from a problem known as vanishing gradients.
It has replaced RNNs in most major areas such as machine translation, speech recognition, and time-series prediction.
Many large tech companies have adopted their own version of Transformers and have made them available to the public.
They are statistical inference engines, which means they capture recurring patterns in sequential data.
They have no understanding of the concepts that those data points present.
This is why you need tons of data to obtain acceptable performance from RNNs.
It can produce interesting text excerpts when you provide it with a cue.
you’re able to read the original articlehere.
