Step 1: Understand what RNNs (Recurrent Neural Networks) are. They are a type of neural network used for processing sequences of data, like sentences or time series.
Step 2: Learn about long-range dependencies. This means that RNNs need to remember information from earlier in the sequence to make sense of later parts.
Step 3: Identify the problem. RNNs can have difficulty remembering information from far back in the sequence.
Step 4: Understand the vanishing gradient problem. This is when the gradients (used to update the network during training) become very small, making it hard for the network to learn from earlier information.
Step 5: Conclude that because of the vanishing gradient problem, RNNs struggle with long-range dependencies.
