Synthetic Tabular Data Generation using Transformers (NVIDIA) Course Overview

Course Prerequisites

Target Audience for Synthetic Tabular Data Generation using Transformers (NVIDIA)

Synthetic Tabular Data Generation using Transformers (NVIDIA) is a comprehensive course designed for professionals with Python and Machine Learning experience to enhance their skills in synthetic data generation using Transformers.

Target Audience and Job Roles:

• Data Scientists
• Machine Learning Engineers
• Data Analysts
• AI Researchers
• Data Engineers
• Deep Learning Specialists
• Financial Analysts
• Business Intelligence Professionals
• Statisticians
• Big Data Developers
• Academicians and Researchers in Data Science

Learning Objectives - What you will Learn in this Synthetic Tabular Data Generation using Transformers (NVIDIA)?

Introduction

In the Synthetic Tabular Data Generation Using Transformers (NVIDIA) course, you'll delve into using Transformers for synthetic data generation. This will encompass data preprocessing, model pre-training, fine-tuning, inference, and evaluation, all aimed at enhancing model performance.

Learning Objectives and Outcomes

• Understand how synthetic data can improve model performance.
• Gain proficiency in using Transformers for Synthetic Data Generation.
• Master the end-to-end development workflow for generating synthetic data using Transformers, including:
  • Data preprocessing
  • Model pre-training
  • Model fine-tuning
  • Inference
  • Evaluation
• Apply the Megatron framework for synthetic data generation.
• Leverage synthetic data generation techniques across various tabular datasets.
• Implement credit card transaction data to practice synthetic data generation.
• Acquire skills to transfer these techniques to other types of tabular data.
• Develop an understanding of language modeling and its application in Transformers.
• Achieve a foundational grasp of generating synthetic tabular data for downstream predictive tasks.

These objectives aim to provide a comprehensive understanding and practical skill set for improving machine learning models through synthetic data generation using cutting-edge Transformer models.