Neural Machine Translation (NMT):

• Sequence-to-Sequence Model (Seq2Seq): Neural machine translation using encoder-decoder architecture. The encoder processes the input sentence, and the decoder generates the translated sentence.

pythonCopy codefrom tensorflow.keras import layers, models

# Build Seq2Seq model
encoder_inputs = layers.Input(shape=(None, input_dim))
encoder = layers.LSTM(256, return_state=True)
encoder_outputs, state_h, state_c = encoder(encoder_inputs)

decoder_inputs = layers.Input(shape=(None, output_dim))
decoder_lstm = layers.LSTM(256, return_sequences=True, return_state=True)
decoder_outputs, _, _ = decoder_lstm(decoder_inputs, initial_state=[state_h, state_c])
decoder_dense = layers.Dense(output_dim, activation='softmax')
decoder_outputs = decoder_dense(decoder_outputs)

model = models.Model([encoder_inputs, decoder_inputs], decoder_outputs)
model.compile(optimizer='adam', loss='categorical_crossentropy')