Natural Language Generation (NLG)

Unlock the Power of AI for Human-Like Text Generation with Advanced NLG Techniques

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MODE
Online/ e-LMS

TYPE
Self Paced

LEVEL
Moderate

DURATION
3 Weeks

About

This program offers a comprehensive exploration of NLG techniques, teaching participants how AI can automatically generate coherent and contextually accurate human language. The program covers language models, neural architectures, ethical considerations, and hands-on projects focused on implementing NLG in real-world applications like automated writing, chatbots, and content creation.

Aim

To provide researchers, AI professionals, and PhD scholars with a deep understanding of Natural Language Generation (NLG), focusing on its architectures, applications, and challenges. This course will cover the theoretical foundations and practical aspects of generating human-like text using AI, from basic models to advanced systems like GPT and BERT.

Program Objectives

Master the fundamentals of NLG and its various architectures.
Build and train models for text generation using state-of-the-art techniques.
Understand the ethical challenges and biases in NLG.
Gain hands-on experience with transformer-based models for NLG tasks.
Explore real-world applications of NLG in content automation and communication.

Program Structure

Introduction to Natural Language Generation
- Overview of NLG
- Applications and Trends in NLG (e.g., Chatbots, Content Generation)
- Key Challenges in NLG
Fundamentals of Natural Language Processing (NLP)
- Tokenization, Lemmatization, and Stemming
- Word Embeddings (Word2Vec, GloVe)
- Sequence Modeling in NLP (Bag of Words, TF-IDF)
Recurrent Neural Networks (RNNs) for NLG
- RNNs and Sequence-to-Sequence Models
- LSTMs and GRUs for Text Generation
- Encoder-Decoder Architectures
Transformers for Language Modeling
- Attention Mechanism and Self-Attention
- Introduction to Transformers
- BERT, GPT, and Their Role in NLG
Advanced Language Models
- GPT-2, GPT-3, and GPT-4 Architectures
- Pretraining and Fine-tuning Techniques
- Comparison of Pretrained Language Models (e.g., T5, BART)
Conditional NLG
- Text Generation with Conditional Inputs (e.g., Text Summarization, Translation)
- Seq2Seq with Attention
- Applications in Machine Translation (MT) and Summarization
Controlling Text Generation
- Controlling Style and Tone in NLG
- Beam Search, Greedy Search, and Sampling Methods
- Top-k and Top-p Sampling
Evaluating NLG Models
- Evaluation Metrics for NLG (BLEU, ROUGE, METEOR)
- Human Evaluation vs. Automated Evaluation
- Challenges in Evaluating Generated Text
Ethics in NLG
- Bias and Fairness in Language Models
- Ethical Considerations in Text Generation
- Misinformation and Abuse of NLG Systems
Fine-Tuning and Deploying NLG Models

Fine-Tuning Large Language Models for Specific Domains
Model Deployment in Real-World Applications
Scaling and Optimizing NLG Models

Case Studies in NLG
- Hands-on Applications (Chatbots, Automated Report Writing)
- Industry Use Cases (Marketing, Healthcare, Journalism)
Final Project
- Build and deploy an NLG model for a specific task (e.g., text summarization, chatbot, or creative writing generator)

Participant’s Eligibility

AI researchers, machine learning engineers, natural language processing (NLP) experts, and academicians focusing on AI and language models.

Program Outcomes

Mastery of NLG techniques using transformer models like GPT and BERT.
Hands-on experience building and fine-tuning NLG systems for real-world tasks.
Understanding of the ethical challenges and considerations in AI-generated text.
Ability to implement NLG for applications like chatbots, automated journalism, and content generation.

Fee Structure

Standard Fee: INR ~~4,998~~ USD 78

Discounted Fee: INR 2,499 USD 39

We are excited to announce that we now accept payments in over 20 global currencies, in addition to USD. Check out our list to see if your preferred currency is supported. Enjoy the convenience and flexibility of paying in your local currency!

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Key Takeaways

Program Assessment

Certification to this program will be based on the evaluation of following assignment (s)/ examinations:

Exam	Weightage
Mid Term Assignments	50 %
Project Report Submission (Includes Mandatory Paper Publication)	50 %

To study the printed/online course material, submit and clear, the mid term assignments, project work/research study (in completion of project work/research study, a final report must be submitted) and the online examination, you are allotted a 1-month period. You will be awarded a certificate, only after successful completion/ and clearance of all the aforesaid assignment(s) and examinations.