Term Paper On Neural Networks

Term Paper On Neural Networks-64
And in cases like speech recognition, waiting till an entire sentence is spoken might make for a less compelling use case.Whereas for NLP tasks, where the inputs tend to be available, we can likely consider entire sentences all at once.I am sure you are quick to point out that we are kinda comparing apples and oranges here.

And in cases like speech recognition, waiting till an entire sentence is spoken might make for a less compelling use case.Whereas for NLP tasks, where the inputs tend to be available, we can likely consider entire sentences all at once.I am sure you are quick to point out that we are kinda comparing apples and oranges here.

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In speech recognition and handwriting recognition tasks, where there could be considerable ambiguity given just one part of the input, we often need to know what’s coming next to better understand the context and detect the present.

This does introduce the obvious challenge of how much into the future we need to look into, because if we have to wait to see all inputs then the entire operation will become costly.

Sure can, but the ‘series’ part of the input means something.

A single input item from the series is related to others and likely has an influence on its neighbors.

While it’s good that the introduction of hidden state enabled us to effectively identify the relationship between the inputs, is there a way we can make a RNN “deep” and gain the multi level abstractions and representations we gain through “depth” in a typical neural network? (1) Perhaps the most obvious of all, is to add hidden states, one on top of another, feeding the output of one to the next.

(2) We can also add additional nonlinear hidden layers between input to hidden state (3) We can increase depth in the hidden to hidden transition (4) We can increase depth in the hidden to output transition.

But what we seemingly lose in value here, we gain back by introducing the “hidden state” that links one input to the next.

The hidden state captures the relationship that neighbors might have with each other in a serial input and it keeps changing in every step, and thus effectively every input undergoes a different transition!

Also, depending on the application, if the sensitivity to immediate and closer neighbors is higher than inputs that come further away, a variant that looks only into a limited future/past can be modeled.

A recurrent neural network parses the inputs in a sequential fashion.

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