Applications Open now for May 2024 Batch | Applications Close: May 26, 2024 | Exam: Jul 07, 2024
Applications Open now for May 2024 Batch | Applications Close: May 26, 2024 | Exam: Jul 07, 2024
Degree Level Course
Introduction to Natural Language Processing (i-NLP)
Natural language (NL) refers to the language spoken/written by humans. NL is the primary mode
of communication for humans. With the growth of the world wide web, data in the form of text
has grown exponentially. It calls for the development of algorithms and techniques for
processing natural language for the automation and development of intelligent machines: Natural
Language Processing (NLP).
On the completing the course, the participant will learn the following:
1. Why is processing language computationally hard and why specialized techniques need
to be developed to process texts?
2. Knowledge and in-depth understanding of linguistics techniques and classical (statistical)
approaches (pre-deep learning era) to NLP and their limitations.
3. Knowledge and in-depth understanding of deep learning approaches (RNN and CNN) to
NLP.
4. Knowledge and in-depth understanding of Attention Mechanism, Transformers and Large
Language Models (LLMs)
5. Ability to read and understand latest NLP-related research papers.
6. Ability to identify applicable NLP technique to solve a real-world problem involving text
processing.
7. Ability to implement NLP models and algorithms for problems related to text processing.
8. Ability to develop applications based on textual generative models (LLMs - Large
Language Models)
For details of standard course structure and assessments, visit
Academics
page.
Introduction to Natural Language (NL)
Why is it hard to process natural language?
Levels of Language Processing
Linguistic Fundamentals for NLP
NLP Pipeline: Tokenization, lemmatization, normalization, POS,
Parsing, etc.
Sub-tokenization
Text Prediction: Introduction, Framework, and its components
Evaluation
Feed Forward Neural Networks for NLP, Regularization, Dropout
Computational Graphs and Backpropagation
Word Representation: Distributed Representations
Language Models: n-gram and Neural
Word2Vec, GloVe
CNNs for NLP
Neural Sequence Models
Contextualized Word Embeddings
Attention Mechanism
Assessment: Hands on assignment
Self-attention Mechanism
Transformers
Pretrained Language Models (PLMs): BERT, GPT, etc.
Fine tuning and transfer learning
Large Language Models (LLMs)
Parameter Efficient Fine Tuning: Prefix-coding, LORA, etc.
Emergent Behavior: In-context learning, Instruction Tuning
RLHF
