Deepu2672/seqtoseq

Sequence-to-Sequence Model

This project implements a Sequence-to-Sequence (Seq2Seq) model, likely for applications in natural language processing (NLP), such as machine translation, text summarization, or other sequence prediction tasks. The notebook provides a step-by-step guide to building and training the model.

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Features

• Implements a Seq2Seq architecture.
• Contains preprocessing steps for input/output sequences.
• Includes detailed explanations and visualizations for key components.
• Supports training, validation, and evaluation of the model.

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Requirements

Ensure the following libraries and dependencies are installed:

• Python (>=3.8)
• NumPy
• TensorFlow or PyTorch (depending on the implementation)