The following image shows a diagram of an RNN.
RNNs are made of neurons: data-processing nodes that work together to perform complex tasks. The neurons are organized as input, output, and hidden layers. The input layer receives the information to process, and the output layer provides the result. Data processing, analysis, and prediction take place in the hidden layer.
Hidden layer
RNNs work by passing the sequential data that they receive to the hidden layers one step at a time. However, they also have a self-looping or recurrent workflow: the hidden layer can remember and use previous inputs for future predictions in a short-term memory component. It uses the current input and the stored memory to predict the next sequence.
For example, consider the sequence: Apple is red. You want the RNN to predict red when it receives the input sequence Apple is. When the hidden layer processes the word Apple, it stores a copy in its memory. Next, when it sees the word is, it recalls Apple from its memory and understands the full sequence: Apple is for context. It can then predict red for improved accuracy. This makes RNNs useful in speech recognition, machine translation, and other language modeling tasks.
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Training
Machine learning (ML) engineers train deep neural networks like RNNs by feeding the model with training data and refining its performance. In ML, the neuron's weights are signals to determine how influential the information learned during training is when predicting the output. Each layer in an RNN shares the same weight.
ML engineers adjust weights to improve prediction accuracy. They use a technique called backpropagation through time (BPTT) to calculate model error and adjust its weight accordingly. BPTT rolls back the output to the previous time step and recalculates the error rate. This way, it can identify which hidden state in the sequence is causing a significant error and readjust the weight to reduce the error margin.
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