Deep Learning for NLP

This course is designed for people with at least introductory knowledge of Python/R and Machine Learning and introduces both basic NLP techniques for extracting information from text as well as Deep Learning models which are reshaping the NLU and NLP industry. Topics covered include automated sentiment and content analysis, question answering, speech to text conversion, etc.

Curriculum

  1. Python/R and machine learning overview
  2. Introduction to Natural Language Processing
  3. Word vector representations
  4. Neural Networks for Named Entity Recognition
  5. Sequence-to-sequence modelling: Recurrent Neural Networks
  6. Sentiment analysis using LSTM networks
  7. Text classification using CNN
  8. Deep Learning for NLP: applications and use cases

Prerequisites

  • Linear Algebra, probability theory and calculus
  • Knowledge of traditional regression and classification models
  • Intermediate knowledge of Python/R

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