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Fine tuning with OpenAI

with
Seth Kramer
Course length:
41 min

What's inside

What you'll learn

  • Learn use-cases for fine-tuning
  • Learn best practices for creating your own model
  • The process of how fine-tuning works
  • How to create fine-tuning datasets
  • How to create your own fine-tuned AI model

Meet your instructor

Seth Kramer

Lorem ipsum dolor sit amet, consectetur adipiscing elit. Suspendisse varius enim in eros elementum tristique. Duis cursus, mi quis viverra ornare, eros dolor interdum nulla, ut commodo diam libero vitae erat. Aenean faucibus nibh et justo cursus id rutrum lorem imperdiet. Nunc ut sem vitae risus tristique posuere.

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Fine tuning with

Fine tuning with OpenAI

In this course, you'll learn how to fine-tune with OpenAI and GPT. You'll understand the best practices and fine-tune your own model!

Course Preview:

About This Course

This course will teach  how to fine-tune pre-trained OpenAI models for specific tasks and use cases. Students will learn how to select the appropriate model architecture and how to prepare and format data for fine-tuning.

They will also learn how to train and evaluate the fine-tuned model, as well as techniques for troubleshooting and improving performance.

By the end of the course, students will have the skills and knowledge necessary to fine-tune OpenAI models for a variety of natural language processing tasks such as text classification, language translation, and text generation.

What you'll learn

  • Learn use-cases for fine-tuning
  • Learn best practices for creating your own model
  • The process of how fine-tuning works
  • How to create fine-tuning datasets
  • How to create your own fine-tuned AI model

Meet your instructor

Seth Kramer

Hi! I'm Seth, the founder and lead instructor at No Code MBA.

I've built massive no-code automations for venture-backed startups, beautiful Webflow-powered websites (including one for a celebrity chef), and bootstrapped a 7-figure marketplace business.

No-code tools have helped me reach my entrepreneurial dreams without writing a line of code, and I'm thrilled to share everything I've learned with you through No Code MBA courses.

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This course includes

14 hours on-demand video

6 real-world projects

Full lifetime access

Certificate of compeition

Fine tuning with OpenAI

In this course, you'll learn how to fine-tune with OpenAI and GPT. You'll understand the best practices and fine-tune your own model!

About
Member Discussion

Course Description

This course will teach  how to fine-tune pre-trained OpenAI models for specific tasks and use cases. Students will learn how to select the appropriate model architecture and how to prepare and format data for fine-tuning.

They will also learn how to train and evaluate the fine-tuned model, as well as techniques for troubleshooting and improving performance.

By the end of the course, students will have the skills and knowledge necessary to fine-tune OpenAI models for a variety of natural language processing tasks such as text classification, language translation, and text generation.

What you'll learn

  • Learn use-cases for fine-tuning
  • Learn best practices for creating your own model
  • The process of how fine-tuning works
  • How to create fine-tuning datasets
  • How to create your own fine-tuned AI model

Meet your instructor

Seth Kramer

Hi! I'm Seth, the founder and lead instructor at No Code MBA.

I've built massive no-code automations for venture-backed startups, beautiful Webflow-powered websites (including one for a celebrity chef), and bootstrapped a 7-figure marketplace business.

No-code tools have helped me reach my entrepreneurial dreams without writing a line of code, and I'm thrilled to share everything I've learned with you through No Code MBA courses.

Lorem ipsum dolor sit amet, consectetur adipiscing elit. Suspendisse varius enim in eros elementum tristique. Duis cursus, mi quis viverra ornare, eros dolor interdum nulla, ut commodo diam libero vitae erat. Aenean faucibus nibh et justo cursus id rutrum lorem imperdiet. Nunc ut sem vitae risus tristique posuere.

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