RNN

RNN stands for Recurrent Neural Network. It's a type of neural network designed for processing sequences and lists of data points that have temporal relationships.

• Sequential Data: RNNs are suitable for data where previous information aids in understanding the present, like time series data, speech, or natural language.

The defining characteristic of RNNs is their looping mechanism, where information cycles through the network, allowing the network to maintain information in "memory" from previous steps.

RNNs and their variants are used in numerous applications including:

• Natural Language Processing (e.g., machine translation, sentiment analysis)
• Time Series Analysis
• Speech Recognition
• Music Generation

RNNs are trained using gradient-based optimization techniques. Because they deal with sequences, a specific variant called Backpropagation Through Time (BPTT) is used.

One of the main challenges with vanilla RNNs is the vanishing and exploding gradient problem. This means that during training, gradients that are back-propagated can either vanish (become very small) or explode (become very large), making the network hard or even impossible to train for longer sequences.

• To handle these challenges, more advanced RNN architectures have been developed, including:
  • LSTM (Long Short-Term Memory): Designed to remember long-term dependencies in data. LSTMs have a series of gates (input, forget, and output) that regulate the flow of information.
  • GRU (Gated Recurrent Unit): A simpler variation of LSTM that uses fewer parameters and performs comparably in many tasks.

<aside> 💡 It's worth noting that while RNNs have been groundbreaking in processing sequences, they have been somewhat surpassed by Transformer-based architectures (like GPT, BERT) in many NLP tasks due to the latter's ability to handle long-range dependencies and parallel processing advantages.

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Why use RNN

RNNs are specifically designed for sequential data. If the data has some form of temporal dynamics or sequence wherein the order and continuity of the data are important, RNNs are more suitable. ANNs, on the other hand, do not consider the order or sequence of data.

• For instance, in time series forecasting, speech recognition, or natural language processing tasks like language modeling, the sequence in which data points appear is crucial.

RNNs can handle variable-length sequences. RNNs can adapt to these variable lengths, whereas standard ANNs require fixed-size input vectors.

• For example, when dealing with sentences in a natural language processing task, not all sentences have the same length.
• Even though this issue of variable-length sequences in ANN can be solved using zero-padding but it’s not worth it as it may cause unneccessary computation.