Synthetic Tabular Data Generation using Transformers (NVIDIA) Course Overview

Synthetic Tabular Data Generation using Transformers (NVIDIA) Course Overview

Unlock the power of Synthetic Tabular Data Generation using Transformers (NVIDIA) in our 8-hour course. Dive into the end-to-end development workflow for creating synthetic data—from data preprocessing to model pre-training, fine-tuning, inference, and evaluation. This course is pivotal for data scientists aiming to enhance model performance using synthetic data.

### Learning Objectives:
- Understand how synthetic data can improve model robustness.
- Utilize Transformers for generating synthetic tabular data.
- Grasp concepts like data preprocessing, model pre-training, fine-tuning, inference, and evaluation.

### Practical Application:
Leverage these skills on real-world datasets, such as credit card transactions, to bolster predictive tasks.

### Course Benefits:
By course completion, you'll proficiently generate synthetic tabular data, enhancing your models' predictive accuracy.

Purchase This Course

Fee On Request

  • Live Training (Duration : 08 Hours)
  • Per Participant
  • Guaranteed-to-Run (GTR)
  • Classroom Training fee on request
  • Select Date
    date-img
  • CST(united states) date-img

Select Time


♱ Excluding VAT/GST

You can request classroom training in any city on any date by Requesting More Information

  • Live Training (Duration : 08 Hours)
  • Per Participant
  • Classroom Training fee on request

♱ Excluding VAT/GST

You can request classroom training in any city on any date by Requesting More Information

Request More Information

Email:  WhatsApp:

Course Prerequisites

Course Prerequisites:


  • Competency in the Python 3 programming language
  • Basic understanding of Machine Learning and Deep Learning concepts and pipelines
  • Experience building Machine Learning models with Tabular data
  • Basic understanding of language modeling and Transformers

These prerequisites ensure that you have the foundational knowledge necessary to successfully engage with and benefit from the Synthetic Tabular Data Generation using Transformers (NVIDIA) course. They are designed to set you up for success and maximize your learning experience.


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.

Suggested Courses

What other information would you like to see on this page?
USD