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  Home > Academic/Programmes > Programme Structure > CSE (2019)
Core/Elective: Elective Semester: 4 Credits: 4
Course Description

Natural language processing (NLP) is one of the most important technologies of the information age. Applications of NLP are everywhere because people communicate mostly everything in language: web search, advertisement, emails, customer service, language translation, radiology reports, etc. Recently, deep learning approaches have obtained very high performance across many different NLP tasks. In this course students will learn to implement, train, debug, visualize and invent their own neural network models. The course provides a deep excursion into cutting-edge research in deep learning applied to NLP

Course Objectives

To understand the neural network approach to learn and process natural language data
To know advanced concepts in natural language processing
To learn to implement, train, debug and visualize deep neural network models for language processing

Course Content

Module I
Word Vectors-Singular Value Decomposition- Skip-gram-Continuous Bag of Words (CBOW)-Negative Sampling- Distributed Representations of Words and Phrases and their CompositionalityEfficient Estimation of Word Representations in Vector Space- Advanced word vector representations- language models-softmax-single layer networks

Module II
Neural Networks and backpropagation for named entity recognition-A Neural Network for Factoid Question Answering over Paragraphs-Grounded Compositional Semantics for Finding and Describing Images with Sentences-Deep Visual-Semantic Alignments for Generating Image Descriptions-Recursive Deep Models for Semantic Compositionality over a Sentiment Treebank

Module III
Introduction to Tensorflow- Large-Scale Machine Learning on Heterogeneous Distributed SystemsRecurrent neural networks for language modeling and Extensions of recurrent neural network language model-Opinion Mining with Deep Recurrent Neural Networks

Module IV
GRUs and LSTMs for machine translation- Recursive neural networks for parsing- Parsing with Compositional Vector Grammars-Subgradient Methods for Structured Prediction-Parsing Natural Scenes and Natural Language with Recursive Neural Networks-Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank-Dynamic Pooling and Unfolding Recursive Autoencoders for Paraphrase Detection-Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks

Module V
Convolutional neural networks for sentence classification- Sequence to Sequence with Neural Networks-Neural Machine Translation by Jointly Learning to Align and Translate- Dynamic Memory Networks for NLP


1. Yoav Goldberg, Neural Network Methods for Natural Language Processing, Morgan & Claypool Publishers, 1ed, 2017
2. Ian Goodfellow, YoshuaBengo, Aaron Courville, Deep Learning, 1e, MIT Press, 2017
3. Nikhil Buduma and Nicholas Locascio, Fundamentals of Deep Learning: Designing NextGeneration Machine Intelligence Algorithms, 1e, Shroff/O'Reilly, 2017
4. Josh Patterson and Adam Gibson, Deep Learning: A Practitioner's Approach, 1e, Shroff/O'Reilly, 2017

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