Natural Language Processing with Deep learning and its applications
Natural Language Processing (NLP) is an field which enables to understand and process human language. This course provides a foundation for understanding NLP concepts, techniques and applications. It covers essential text preprocessing methods, word representations, syntactic and semantic analysis, and language modelling approaches. This course will also introduce to deep learning NLP Models including transformers like BERT and GPT.
Learning Outcomes
On completion, students will be able to:
- Understanding NLP Fundamentals
- Text preprocessing skills
- Word representation techniques
- Syntactic and Semantic Analysis
- Language modelling
Course Contents (Academic schedule)
Overview of NLP – Definitions, applications and challenges in Processing human language, Text preprocessing, Word Representation, What do you understand by Syntax and Parsing, Different Language modelling, Text classification, Named Entity Recognition, Machine Translation, Speech Processing Basics, Transformers and Deep Learning in NLP
- Essential linear algebra for deep learning
- Fundamentals of linear classification: weights, bias, scores, and loss functions
- Calculus for the gradient descent algorithm
- Forward and backward propagation with regularization
- Batch processing for large datasets
- Linear to nonlinear classification via activation functions
- Computational setup of a shallow neural network
- Tuning neural network performance
- Pre-processing data and batch normalization
- Cross-validation for validating model performance
- Extending the computational setup from a shallow to a deep neural network
- Introduction to the TensorFlow library
- Application projects: implementing shallow and deep neural network models using TensorFlow; implementing machine learning models on edge devices using Edge Impulse.
Pre-requisites
- Knowledge of python programming
Name of the Coordinator
Program Highlights
- Core Natural Language Processing Concepts
- Practical Skills in understanding NLP in different domains
- Real-world applications of Deep Learning in NLP