Introduction to Transformer Based Natural Language Processing (NVIDIA) Course Overview

Last Updated : 18 Apr 2025

Introduction to Transformer Based Natural Language Processing (NVIDIA) Course Overview

Introduction to Transformer Based Natural Language Processing (NVIDIA) Course

Unlock the power of transformer-based large language models (LLMs) with our comprehensive Introduction to Transformer Based Natural Language Processing (NVIDIA) course. Over 8 hours, you'll explore how Transformers serve as the foundation for modern LLMs, enabling you to tackle various NLP tasks effectively. By the end, you'll master practical applications like text classification, named-entity recognition (NER), author attribution, and question answering.

Learning Objectives:
- Grasp how Transformers function as the core of modern LLMs.
- Utilize pre-trained LLMs to manipulate, analyze, and generate text.
- Apply Transformer-based models to solve key NLP tasks.

Equip yourself with the skills to create interactive, human-like AI applications that revolutionize user experiences.

Intermediate

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Flexi Video	16,449
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Total Fees (without exam & Labs)	22,359 (INR)
Total Fees (with Labs)	28,359 (INR)

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Schedule personalized sessions based upon your availability.

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Experience exceptional training with the confidence of our Happiness Guarantee, ensuring your satisfaction or a full refund.

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Immerse yourself in a focused learning environment, free from distractions, where you can sharpen your skills in popular global destinations.

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Course Prerequisites

Minimum Required Prerequisites for Introduction to Transformer Based Natural Language Processing (NVIDIA) Course

To ensure you have the best learning experience in our "Introduction to Transformer Based Natural Language Processing (NVIDIA)" course, it is important to have a foundational understanding in the following areas:

Basic Understanding of Deep Learning Concepts: Familiarity with the core principles of deep learning, including neural networks, backpropagation, and optimization techniques.
Basic Understanding of Language Modelling and Transformers: Basic knowledge of NLP concepts such as tokenization, embeddings, and an introductory understanding of Transformer architecture, including mechanisms like attention.

These prerequisites are designed to prepare you for the advanced concepts covered in the course without overwhelming beginners. If you have a fundamental grasp of these areas, you will be well-equipped to undertake this training successfully.

Target Audience for Introduction to Transformer Based Natural Language Processing (NVIDIA)

Introduction

The "Introduction to Transformer Based Natural Language Processing" course by NVIDIA is designed for individuals with a basic understanding of deep learning and language modelling, focusing on using Transformers in modern NLP tasks.

Target Audience and Job Roles

Data Scientists
Machine Learning Engineers
NLP Specialists
AI Researchers
Software Developers
AI Enthusiasts
Research Scientists
Technical Managers
Data Analysts
Computer Science Students
Computational Linguists
IT Professionals specializing in AI
Chatbot Developers
Academic Researchers in AI and NLP
AI Product Managers
AI Consultants

Learning Objectives - What you will Learn in this Introduction to Transformer Based Natural Language Processing (NVIDIA)?

Brief Introduction: The "Introduction to Transformer Based Natural Language Processing (NVIDIA)" course offers an 8-hour deep dive into leveraging Transformers to build and deploy powerful NLP applications for tasks like text classification, named-entity recognition (NER), author attribution, and question answering.
Learning Objectives and Outcomes:

Understand how Transformers serve as the foundational building blocks of modern large language models (LLMs) for NLP applications.
Learn the manipulation, analysis, and generation of text-based data using Transformer-based LLMs.
Leverage pre-trained modern LLMs for token classification, text classification, summarization, and question answering.
Explore practical applications of Transformers in NLP tasks such as author attribution.
Develop skills to fine-tune and deploy Transformer-based models for various real-world applications.
Gain hands-on experience with various NLP tasks using state-of-the-art Transformer models.
Understand the role of LLMs in creating interactive human-machine experiences.
Explore advancements in NLP and generative AI driven by Transformer-based LLMs.
Learn best practices for deploying and optimizing Transformer models in various NLP applications.
Achieve a foundational understanding of deep learning and language modeling prerequisites essential for working with Transformers.