RNN (Recurrent Neural Network), CNN (Convolutional Neural Network), LSTM (Long-Short Term Memory), and GRU (Gated Recurrent Unit) are all types of neural networks used for different tasks in the field of deep learning.
- RNNs are used for sequence-to-sequence problems, such as language translation, where the input is a sequence of words and the output is a sequence of words in a different language.
- CNNs are used for image classification and other computer vision tasks, where the input is an image and the output is a label or a set of labels that describe the content of the image.
- LSTMs are a type of RNN designed to handle the vanishing gradient problem, which is a common problem in traditional RNNs. LSTMs are used for sequence-to-sequence problems with long-term dependencies, where the model needs to remember information from earlier in the sequence in order to make predictions later on.
- GRUs are another type of RNN that, like LSTMs, are designed to handle the vanishing gradient problem. GRUs are computationally more efficient than LSTMs, and are often used for the same types of problems as LSTMs.
