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Course/Module 6/Topic 4 of 4Beginner

Sequence-to-Sequence Models

Explore Seq2Seq models for translation and text generation.

75 minBy Priygop TeamLast updated: Feb 2026

Applications

  • Machine Translation
  • Chatbots
  • Text Summarization

Simple Seq2Seq with Keras

Example
from tensorflow import keras

encoder_inputs = keras.layers.Input(shape=(None,))
x = keras.layers.Embedding(1000, 64)(encoder_inputs)
encoder_outputs, state_h, state_c = keras.layers.LSTM(64, return_state=True)(x)
encoder_states = [state_h, state_c]

decoder_inputs = keras.layers.Input(shape=(None,))
x = keras.layers.Embedding(1000, 64)(decoder_inputs)
decoder_lstm = keras.layers.LSTM(64, return_sequences=True, return_state=True)
decoder_outputs, _, _ = decoder_lstm(x, initial_state=encoder_states)
decoder_dense = keras.layers.Dense(1000, activation='softmax')
decoder_outputs = decoder_dense(decoder_outputs)

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

Try It Yourself — Natural Language Processing

Try It Yourself — Natural Language ProcessingHTML
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Quick Quiz — Natural Language Processing

Additional Resources

Recommended Reading

  • Speech and Language Processing by Jurafsky & Martin
  • Natural Language Processing with Python (Bird, Klein, Loper)
  • Deep Learning for NLP with PyTorch

Online Resources

  • TensorFlow NLP Tutorials
  • NLTK Documentation
  • Hugging Face Transformers
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