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A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course.

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A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course.

Unlock the power of the OpenAI API to build powerful applications and fine-tune large language models (LLMs). By the end of the course, you'll be equipped with the skills to set up your environment, explore the fundamentals of transformers, and create your own intelligent applications. You’ll also dive deep into prompt engineering, sentiment analysis, and even computer vision, using practical examples to reinforce your knowledge.

You'll start by setting up your environment and understanding key concepts, such as AI definitions and acronyms, before diving into the OpenAI API itself. The course also walks you through hands-on examples, such as translating articles, summarizing text, and building a chatbot. As you progress, you'll use cutting-edge OpenAI tools, including text-to-speech, image generation, and computer vision APIs to add even more advanced functionalities to your applications.

Towards the end, you will fine-tune your models, deploy applications, and ensure ethical AI usage. By the time you finish, you'll be able to build sophisticated AI-powered applications that leverage OpenAI's vast capabilities, with an emphasis on real-world use cases and deployment.

This course is ideal for developers, AI enthusiasts, or anyone interested in working with OpenAI’s advanced language models and APIs. Basic programming skills and familiarity with AI concepts will be beneficial, but anyone eager to explore AI’s potential is welcome to join.

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Syllabus

Introduction
In this module, we will introduce you to the course, outline the prerequisites, and walk you through the course syllabus. You'll also familiarize yourself with essential AI definitions and acronyms, ensuring you're ready for the journey ahead.
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Career center

Learners who complete Build Apps and Fine-Tune LLMs Using the OpenAI API will develop knowledge and skills that may be useful to these careers:

Reading list

We haven't picked any books for this reading list yet.
Comprehensive guide to reinforcement learning using the OpenAI Gym environment in Chinese. It covers the basics of reinforcement learning, as well as more advanced topics such as deep reinforcement learning and multi-agent reinforcement learning.
Provides a comprehensive overview of generative adversarial networks (GANs), a type of deep learning model that can generate new data from a given dataset.
Comprehensive guide to computer vision using the OpenAI API. It covers the basics of computer vision, as well as more advanced topics such as object detection and image segmentation.
Hands-on guide to building deep learning models using the OpenAI API. It is aimed at beginners and provides clear and concise explanations of the concepts and techniques involved.
This beginner-friendly guide focuses on the use of transformers in NLP, providing a solid foundation for understanding the inner workings of LLMs.
This comprehensive handbook includes a chapter on LLMs, providing a thorough overview of their history, evolution, and applications.
This collection of papers presents cutting-edge research on LLMs, exploring their capabilities and potential applications in various NLP tasks.
Offers a comprehensive overview of LLMs, covering their theoretical foundations, practical applications, and future directions.
Focuses on the use of prompt engineering for recommendation systems. It is written by Masashi Sugiyama, a leading researcher in the field of recommendation systems.
Focuses on the use of prompt engineering for natural language processing. It is written by Thomas Wolf, a leading researcher in the field of NLP.
While not solely focused on prompt engineering, this book provides a strong foundation in understanding how LLMs work, which is essential for effective prompting. It's suitable for undergraduate and graduate students, offering technical insights into language understanding and generation. It serves as valuable background reading for those wanting to understand the underlying mechanisms of the models they are prompting. Expected publication in September 2024.
Offers a practical, hands-on approach to prompt engineering specifically with ChatGPT. It's an excellent resource for high school and undergraduate students getting started, providing clear examples and exercises. It serves as a useful introductory guide and additional reading to complement foundational AI courses.
Provides a comprehensive guide to prompt engineering, covering techniques for crafting effective inputs to generative AI models. It's particularly useful for understanding how to obtain reliable and predictable results, which is crucial for both beginners and those looking to deepen their practical skills. This book is valuable as a current reference for anyone working with generative AI.
Focuses on the use of prompt engineering for education. It is written by Salman Khan, a leading researcher in the field of education.
Covers the use of prompt engineering for finance. It is written by Richard Roll, a leading researcher in the field of finance.
For those who want to understand the mechanics of LLMs deeply, this book guides you through building one from scratch. This is highly technical and suitable for advanced undergraduate students, graduate students, and researchers. A deep understanding of LLM architecture is beneficial for advanced prompt engineering techniques.

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