Deep Learning for Natural Language Processing

Natural Language Processing (NLP) has become an integral part of the AI revolution. This course covers the fundamentals of deep learning techniques applied to NLP tasks.

Course Outline

• Introduction to NLP 📚

  • Overview of NLP
  • History and evolution of NLP

• Basics of Deep Learning 🧠

  • Neural networks
  • Recurrent Neural Networks (RNNs)
  • Long Short-Term Memory networks (LSTMs)
  • Gated Recurrent Units (GRUs)

• Word Embeddings 📖

  • Types of embeddings
  • Word2Vec
  • GloVe

• Sequence Modeling 📈

  • RNNs for sequence prediction
  • LSTMs and GRUs for time series analysis
  • Attention mechanisms

• NLP Applications 🌐

  • Sentiment Analysis
  • Machine Translation
  • Text Classification

• Practical Projects 🏢

  • Build and deploy your own NLP model

Learning Objectives

• Understand the core concepts of NLP
• Apply deep learning techniques to NLP problems
• Develop and evaluate NLP models

Course Materials

• Video lectures
• Jupyter notebooks
• Assignments
• Quizzes

Recommended Resources

• Natural Language Processing with Python

Images

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