## What is Bidirectional LSTM?

A Bidirectional LSTM (Long Short-Term Memory) is a type of recurrent neural network (RNN) architecture that consists of two separate LSTMs, one processing the input sequence in the forward direction and the other processing it in the reverse direction. This bidirectional structure allows the model to capture both past and future context when making predictions at each time step, making it particularly effective for sequence-to-sequence tasks, such as machine translation, speech recognition, and text summarization.

## How does Bidirectional LSTM work?

In a Bidirectional LSTM, the forward LSTM processes the input sequence in its natural order (from left to right), while the backward LSTM processes the sequence in reverse order (from right to left). The hidden states from both LSTMs are combined at each time step, typically by concatenation, sum, or averaging, to produce a final output. This combination of forward and backward context allows the model to make more informed predictions at each time step.

## Example of Bidirectional LSTM in Python using Keras

```
import numpy as np
from keras.models import Sequential
from keras.layers import Embedding, Bidirectional, LSTM, Dense
# Create a simple bidirectional LSTM model
model = Sequential()
model.add(Embedding(input_dim=10000, output_dim=128))
model.add(Bidirectional(LSTM(units=64, return_sequences=True)))
model.add(Bidirectional(LSTM(units=64)))
model.add(Dense(units=1, activation='sigmoid'))
# Compile the model
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
# Dummy input data and labels for binary classification
x_train = np.random.randint(0, 10000, size=(1000, 100))
y_train = np.random.randint(0, 2, size=(1000, 1))
# Train the model
model.fit(x_train, y_train, epochs=10, batch_size=32)
```