Introduction to Neural Networks
Recurrent Neural Networks (RNNs) are a class of neural networks that are designed to handle sequential data. Unlike feedforward neural networks, which process all the input data at once, RNNs process the input data sequentially, one element at a time. This makes them useful for processing time-series data, natural language processing, and other sequential data.
The key feature of RNNs is that they have a feedback loop that allows them to store information about previous inputs. This feedback loop connects the output of the network at time t-1 to the input of the network at time t. This allows the network to use information from previous inputs to help it process current inputs.
There are many different types of RNNs, including:
Simple RNNs are the most basic type of RNN, but they suffer from the vanishing gradient problem, which makes it difficult to train them to recognize long-term dependencies. LSTM and GRU networks were developed to address this problem, and they are now the most commonly used types of RNNs.
Here's an example of how an RNN might be used for natural language processing. Let's say we want to train an RNN to predict the next word in a sentence. We would start by feeding the first word of a sentence into the network. The network would process this input and produce an output. We would then feed the second word of the sentence into the network, along with the output from the previous step. The network would process this input and produce another output. We would continue this process until we had fed all the words in the sentence into the network. At this point, the network would have produced an output for each word in the sentence, and we could use these outputs to predict the next word in the sentence.
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