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We're here to help you find itSynthetic 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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Flexi Video | 16,449 |
Official E-coursebook | |
Exam Voucher (optional) | |
Hands-On-Labs2 | 4,159 |
+ GST 18% | 4,259 |
Total Fees (without exam & Labs) |
22,359 (INR) |
Total Fees (with Labs) |
28,359 (INR) |
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Course Prerequisites:
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.
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:
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.
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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