Sequence-to-Sequence Tutorial with TensorFlow

Welcome to the Sequence-to-Sequence (Seq2Seq) tutorial! This guide will walk you through implementing classic Seq2Seq models using TensorFlow, a powerful framework for machine learning and deep learning.

📘 What is Sequence-to-Sequence?

Seq2Seq is a neural network architecture that maps an input sequence to an output sequence. It's widely used in tasks like:

• Machine Translation (e.g., English to French)
• Chatbots (response generation)
• Text Summarization
• Speech Recognition

🧠 Key Components:

• Encoder: Processes the input sequence
• Decoder: Generates the output sequence
• Attention Mechanism: Enhances context understanding

📚 Example Implementations

Here are popular Seq2Seq examples in TensorFlow:

1. Basic RNN Seq2Seq

   # Simple example code
   import tensorflow as tf
   model = tf.keras.Sequential([
       tf.keras.layers.Embedding(input_vocab_size, embedding_dim, input_length=max_length),
       tf.keras.layers.SimpleRNN(units=hidden_units, return_sequences=True),
       tf.keras.layers.Dense(output_vocab_size)
   ])
   

   

2. Attention-Based Model

   

   Attention Visualization

3. Transformer Architecture

   • Introduced in 2017 by Google
   • Uses self-attention and positional encoding
   • Learn more about Transformers

🧩 Practical Tips

• Use tf.keras.layers.LSTM or tf.keras.layers.GRU for better performance
• Experiment with different attention types: tf.keras.layers.Attention()
• Check out this tutorial for foundational concepts

🌐 Applications

Seq2Seq models power real-world applications like:

• 📖 Text summarization with TF-Hub
• 🗣️ Speech-to-text systems
• 🤖 Dialogue systems and Q&A bots
• 📌 Time series prediction

Would you like to dive deeper into any specific implementation or application?