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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.

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  • Live Training (Duration : 08 Hours)
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Free Pre-requisite Training

Join a free session to assess your readiness for the course. This session will help you understand the course structure and evaluate your current knowledge level to start with confidence.

Assessments (Qubits)

Take assessments to measure your progress clearly. Koenig's Qubits assessments identify your strengths and areas for improvement, helping you focus effectively on your learning goals.

Post Training Reports

Receive comprehensive post-training reports summarizing your performance. These reports offer clear feedback and recommendations to help you confidently take the next steps in your learning journey.

Class Recordings

Get access to class recordings anytime. These recordings let you revisit key concepts and ensure you never miss important details, supporting your learning even after class ends.

Free Lab Extensions

Extend your lab time at no extra cost. With free lab extensions, you get additional practice to sharpen your skills, ensuring thorough understanding and mastery of practical tasks.

Free Revision Classes

Join our free revision classes to reinforce your learning. These classes revisit important topics, clarify doubts, and help solidify your understanding for better training outcomes.

Inclusions in Koenig's Learning Stack may vary as per policies of OEMs

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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.

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