Bidirectional LSTM

Bidirectional Long Short-Term Memory (BiLSTM) is a type of recurrent neural network (RNN) architecture that processes sequential data in both forward and backward directions to capture context from past and future states. It enhances traditional LSTMs by using two separate hidden layers—one for forward and one for backward sequences—and combining their outputs. This makes it particularly effective for tasks where understanding the full context of a sequence is crucial, such as natural language processing.

Also known as: BiLSTM, Bidirectional Long Short-Term Memory, Bi-directional LSTM, Bidirectional Lstm, Bi-LSTM

Why learn Bidirectional LSTM?

Developers should learn and use Bidirectional LSTM when working on sequence modeling tasks that benefit from contextual information from both directions, such as named entity recognition, machine translation, and speech recognition. It is especially valuable in natural language processing applications where the meaning of a word or phrase depends on surrounding words, as it improves accuracy by leveraging future context in addition to past information.

Learning Resources

• tutorial: Understanding LSTM Networks
• tutorial: Bidirectional LSTM Explained
• docs: TensorFlow Bidirectional LSTM Guide
• course: Deep Learning Specialization (Coursera)