Feedforward neural networks (FNNs) and recurrent neural networks (RNNs) differ primarily in how they process data. FNNs operate with a strict unidirectional flow of information: input data passes through layers (input, hidden, output) without cycles or loops. Each layer’s outputs feed directly into the next layer, and the network has no memory of previous inputs. RNNs, in contrast, introduce cycles in their architecture, allowing information to persist across time steps. This makes RNNs inherently suited for sequential data, where the order and context of inputs matter. The key distinction lies in their handling of temporal or sequential dependencies—FNNs treat each input independently, while RNNs use internal state (or memory) to incorporate past information.
FNNs are ideal for tasks where input data has no inherent sequence or time-based relationship. For example, image classification (e.g., categorizing a photo as a cat or dog) relies on static pixel data processed through fixed layers. Each image is analyzed independently, and the network’s parameters are updated via backpropagation without considering prior inputs. FNN architectures like multilayer perceptrons (MLPs) or convolutional neural networks (CNNs) excel here due to their simplicity and efficiency in transforming inputs to outputs through layered feature extraction. However, FNNs struggle with tasks requiring context from earlier data points, such as predicting the next word in a sentence, because they cannot retain information between inputs.
RNNs address this limitation by maintaining a hidden state that captures information from previous steps in a sequence. For instance, in text generation, an RNN processes each word in a sentence while updating its internal state, allowing it to influence subsequent predictions. This makes RNNs effective for time-series forecasting, speech recognition, or machine translation. However, traditional RNNs face challenges like vanishing gradients, which limit their ability to learn long-range dependencies. Variants like LSTMs (Long Short-Term Memory networks) or GRUs (Gated Recurrent Units) mitigate this by introducing gating mechanisms to control information flow. While RNNs are more flexible for sequential tasks, they are computationally heavier and harder to train compared to FNNs, which remain the go-to for non-sequential problems.