🧠 Introduction to Deep Learning in Natural Language Processing (NLP)

Deep learning has revolutionized the field of NLP, enabling breakthroughs in understanding and generating human language. From language modeling to machine translation, neural networks now power some of the most advanced NLP applications.

Key Concepts

  • Recurrent Neural Networks (RNNs):
    📌 Used for sequential data processing (e.g., text).

    Recurrent_Neural_Network

  • Convolutional Neural Networks (CNNs):
    📌 Applied for feature extraction in text and speech.

    Convolutional_Neural_Network

  • Transformer Models:
    📌 State-of-the-art architecture for attention mechanisms.

    Transformer_Model

Applications

🤖 Real-world use cases:

  • Machine Translation (e.g., Google Translate)
  • Text Generation (e.g., chatbots, story writing)
  • Sentiment Analysis (e.g., social media monitoring)
  • Question Answering (e.g., virtual assistants)
NLP_Applications

Learning Resources

📚 Recommended materials:

  1. Deep Learning for NLP: Foundations and Applications
  2. Books on NLP
  3. Research Papers

🔗 Expand your knowledge: Explore more about NLP techniques