Deep Learning has revolutionized the field of Natural Language Processing (NLP), enabling machines to understand and generate human language with unprecedented accuracy. This course covers the fundamentals of deep learning and its applications in NLP.

Course Outline

  • Introduction to Deep Learning
  • Basic Concepts in NLP
  • Convolutional Neural Networks (CNNs) for Text
  • Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) Networks
  • Sequence-to-Sequence Models
  • Transformers and Attention Mechanisms
  • Applications of Deep Learning in NLP

Learning Objectives

  • Understand the basic concepts of deep learning and their application in NLP.
  • Learn to implement various deep learning models for NLP tasks.
  • Gain hands-on experience with popular deep learning frameworks like TensorFlow and PyTorch.
  • Explore real-world applications of deep learning in NLP, such as machine translation, sentiment analysis, and chatbots.

Related Resources

For further reading and practical exercises, check out our Deep Learning for NLP Tutorial.

Course Materials

Images

Here are some examples of deep learning architectures used in NLP:

Convolutional_Neural_Networks
Recurrent_Neural_Networks
Transformers