LLM Fine Tuning on OpenAI
Free Download LLM Fine Tuning on OpenAI
Published 11/2023
Created by Jose Portilla
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 12 Lectures ( 1h 49m ) | Size: 660 MB
Learn to use OpenAI to fine tune LLMs on your own datasets!
What you’ll learn
Gain a comprehensive understanding of the fundamental principles and advanced concepts in artificial intelligence and language modeling.
Explore various datasets specific to different fields, learning how to identify and understand their unique structures and challenges.
Recognize and understand the various strategies and techniques used in fine-tuning language models for specialized applications.
Master the skills necessary to preprocess datasets effectively, ensuring they are in the ideal format for AI training.
Delve into various methods to enhance the accuracy and efficiency of AI models for specific domains.
Learn to assess the proficiency of fine-tuned models using different evaluation metrics and methods.
Investigate the vast potential of fine-tuned AI models in practical, real-world scenarios across multiple industries.
Acquire knowledge on how to estimate and manage the costs associated with AI model training, making the process efficient and economical.
Requirements
Python experience required, including pandas and basic API knowledge
Experience with the OpenAI API basics
Description
Unleash the Potential of Tailored AI Understanding: Master the Art of Fine-Tuning AI Models Across Diverse Fields Welcome to the Advanced Realm of AI Training!About the Course: Dive into the sophisticated world of AI and language models with our comprehensive course. Here, you’ll learn how to fine-tune OpenAI’s state-of-the-art language models for a variety of specialized fields. Whether you’re a professional in healthcare, finance, education, or another domain, or a researcher or student keen on exploring the depths of AI language comprehension, this course is your key to mastering domain-specific AI language understanding.Course Content: You’ll begin by exploring the intricacies of domain-specific datasets, learning how to dissect and understand the unique structures and challenges they present. The course then guides you through refining these datasets to prime them for AI training. You’ll gain hands-on experience in fine-tuning techniques, learning how to tweak and enhance AI models for domain-specific accuracy. We’ll also cover performance evaluation, offering strategies to assess and boost your model’s effectiveness in your chosen field. Moreover, the course delves into the real-world applications of your fine-tuned model, showcasing its potential across various industries.Course Highlights: Experience practical, hands-on training with real-world data in your field of interest. Our expert-led guidance walks you through every step of dataset preparation and model tuning. You’ll engage with dynamic learning tools like Jupyter Notebooks for an interactive educational experience, gaining rich insights into the challenges and solutions in training AI for specialized domains. This cost-effective training also teaches you how to estimate and manage AI training expenses efficiently.Who Should Enroll: Professionals in various fields seeking to integrate AI tools for enhanced data analysis, researchers and students in specialized areas looking to deepen their AI knowledge, and AI enthusiasts eager to explore domain-specific model training.Course Outcome: By the end of this course, you’ll have fine-tuned a sophisticated language model, boosting its proficiency in your specific area of interest. You’ll possess the skills to navigate and utilize AI across various sectors, paving the way for innovative applications and research opportunities.Enroll now and begin your journey towards mastering domain-specific AI and transforming industries with your expertise!
Who this course is for
Experienced Python developers looking to learn how to fine-tune OpenAI LLMs
Homepage
https://www.udemy.com/course/llm-fine-tuning-on-openai/
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