Introduction to Sequence Data

In the realm of artificial intelligence and machine learning, many real-world problems involve data that has a sequential nature. This means the order of data points is crucial and carries significant meaning. Examples include text (words in a sentence), time series data (stock prices over time), speech, and video frames.

Traditional feedforward neural networks struggle with sequential data because they treat each input independently. They lack a mechanism to "remember" past information, which is vital for understanding context and making predictions based on history.

Recurrent Neural Networks (RNNs)

Recurrent Neural Networks (RNNs) are a class of neural networks specifically designed to handle sequential data. Their core innovation lies in their ability to maintain an internal "state" or "memory" that is updated at each step of the sequence.

Unrolled RNN diagram

An unrolled RNN, showing how information flows through time.

At each time step $t$, an RNN receives an input $x_t$ and the hidden state $h_{t-1}$ from the previous time step. It then produces an output $y_t$ and updates its hidden state to $h_t$, which is passed to the next time step.

The recurrent connection allows the network to learn dependencies across time. However, standard RNNs suffer from the vanishing gradient problem, making it difficult for them to learn long-term dependencies. This means they tend to "forget" information from many steps in the past.

Long Short-Term Memory (LSTM) Networks

Long Short-Term Memory (LSTM) networks are a special type of RNN designed to overcome the vanishing gradient problem and effectively capture long-range dependencies. LSTMs achieve this through a more complex internal structure called a "cell," which includes various "gates" that regulate the flow of information.

LSTM cell diagram

A simplified diagram of an LSTM cell with its gates.

The key components of an LSTM cell are:

  • Forget Gate: Decides what information to throw away from the cell state.
  • Input Gate: Decides which new information to store in the cell state.
  • Output Gate: Decides what to output based on the cell state.

These gates, implemented using sigmoid and tanh activation functions, allow LSTMs to selectively remember or forget information over long periods, making them highly effective for tasks like language modeling, machine translation, and speech recognition.

Common Applications

  • Natural Language Processing (NLP): Machine translation, sentiment analysis, text generation, question answering.
  • Speech Recognition: Converting spoken language into text.
  • Time Series Analysis: Stock market prediction, weather forecasting, anomaly detection.
  • Video Analysis: Action recognition, video captioning.
  • Music Generation: Composing new musical pieces.

Try a Simple Sequence Prediction (Conceptual)

Imagine predicting the next letter in a word. LSTMs can learn patterns like this.

Input a short sequence:

Enter a sequence and click "Predict Next".

Further Learning Resources

This module is part of a broader curriculum on AI and Machine Learning. For more in-depth understanding and practical examples, explore the following: