Natural Language Processing (NLP) and Generative AI

This repository showcases practical implementations of various Natural Language Processing (NLP) and Generative AI techniques. It includes both foundational methods and advanced architectures, demonstrating a comprehensive approach to understanding and applying these technologies.

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Traditional NLP Techniques

• Counting Tokens: Tokenizing text and counting tokens for preprocessing tasks.
• Bag-of-Words & TF-IDF: Creating document-term matrices and extracting meaningful features from text.

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Word Embeddings

• Word2Vec: Generating dense vector representations that capture word semantics and relationships.

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Advanced Architectures

• LSTM (Long Short-Term Memory): Sequence modeling for tasks like text generation and sentiment analysis.
• Autoencoders: Compressing and reconstructing text data for unsupervised learning applications.
• Seq2Seq (Sequence-to-Sequence): Building models for tasks such as machine translation, text summarization, and more.
• BERT (Bidirectional Encoder Representations from Transformers): Fine-tuning state-of-the-art models for advanced NLP applications.