Machine Learning Tutorials, Courses and Certifications
RNN Intro¶
A Recurrent Neural Network (RNN) is a type of artificial neural network designed to handle data where the order of information is important, like sentences or time series. It has a “memory” that helps it remember past inputs, allowing it to make better predictions based on previous data. This makes RNNs useful for tasks like language translation, speech recognition, and predicting future trends. Unlike regular neural networks, RNNs can process sequences of data by looping information back into the model.
RNN – working¶
Recurrent Neural Networks (RNNs) work by processing data step-by-step, remembering what happened before through a hidden state, like a memory. As they go through each part of a sequence (like words in a sentence), they update this memory and use it to help make predictions or decisions about the next part. This allows RNNs to understand the order and context in data like text or time series.
Recurrent Neural Networks (RNNs) work by processing data in a sequence, one step at a time. Here’s how they operate:
Input Sequence: At each time step, the RNN takes an input from the sequence (like a word in a sentence or a data point in a time series).
Hidden State: The RNN has a hidden state, which acts as its memory. This hidden state is updated at each time step based on the current input and the previous hidden state.
Looping: The updated hidden state is then fed back into the network along with the next input in the sequence. This allows the RNN to “remember” information from earlier in the sequence.
Output: At each step, the RNN can produce an output, which could be a prediction or classification based on the sequence so far.
Training: During training, the network adjusts its weights to minimize the error between its predictions and the actual sequence data. This is done using a method called Backpropagation Through Time (BPTT), which unrolls the network over the entire sequence.
This looped structure enables RNNs to handle sequential data effectively, capturing patterns and dependencies that unfold over time.
