Disclaimer: Please note that this repository is a work in progress, and its content is subject to change. The majority of the material, including the documentation and example notebooks, has been generated using OpenAI's ChatGPT. As such, some content may require further refinement or updates from human developers to ensure accuracy and alignment with best practices.
This repository contains a collection of Jupyter Notebooks that showcase the integration and exploration of OpenAI's ChatGPT with Jupyter Notebooks. The notebooks serve as examples and tutorials for users interested in utilizing ChatGPT's powerful natural language processing capabilities within the interactive and visual environment provided by Jupyter Notebooks.
The openai-notebooks repository aims to provide users with a diverse set of examples that demonstrate how ChatGPT can be used for various tasks and in different domains. The goal is to enable researchers, developers, and data scientists to harness the power of ChatGPT for their projects and experiments, while also fostering an open-source community that can contribute to and benefit from these notebooks.
By providing a wide range of examples and use cases, this repository will help users familiarize themselves with ChatGPT, as well as offer inspiration for their own projects. Through these notebooks, users can learn how to interface with ChatGPT and experiment with its capabilities, making it easier to integrate the model into their own work.
Here is a list of example notebooks showcasing various ways to interface with ChatGPT. Note that all notebooks are prefixed with "chatgpt_":
chatgpt_basic_usage.ipynb: Demonstrates how to make simple API calls to ChatGPT and display the results.chatgpt_conversation.ipynb: Showcases an interactive conversation with ChatGPT, simulating a chatbot experience.chatgpt_summarization.ipynb: Explores using ChatGPT to summarize text documents.chatgpt_translation.ipynb: Demonstrates how ChatGPT can be used for language translation tasks.chatgpt_sentiment_analysis.ipynb: Shows how to perform sentiment analysis on text using ChatGPT.chatgpt_image_generation.ipynb: Demonstrates how to generate images using ChatGPT by providing textual descriptions. This notebook shows how to use ChatGPT to create image prompts and then use an image generation model like DALL-E or VQGAN to generate the actual images.chatgpt_question_answering.ipynb: Showcases how to use ChatGPT for question-answering tasks, such as retrieving answers from a given document or passage.chatgpt_text_classification.ipynb: Explores how ChatGPT can be used for text classification tasks, such as categorizing movie reviews or news articles into predefined categories.chatgpt_code_generation.ipynb: Demonstrates using ChatGPT for generating code snippets based on natural language descriptions of desired functionality.
Each notebook provides a step-by-step guide on how to use ChatGPT for the specific task mentioned, with detailed explanations and code examples. This allows users to quickly understand and experiment with various applications of ChatGPT.
This project is licensed under the MIT License. See the LICENSE file for more information. Getting Started
To get started with the openai-notebooks repository, clone the repository using the following command:
git clone https://github.com/dapperfu/openai-notebooks.gitOnce the repository is cloned, navigate to the project directory and follow the instructions provided in the individual notebooks to set up your environment and begin exploring ChatGPT with Jupyter Notebooks.
This openai-notebooks repository has been primarily generated using OpenAI's ChatGPT, showcasing the potential of language models to assist in the creation of project documentation and content. ChatGPT has been utilized throughout the development process, from generating the initial project ideas to writing the README file and even helping create the example Jupyter Notebooks. By leveraging ChatGPT, this project demonstrates how AI can be used to support and streamline various tasks in software development.
As an assistive technology, ChatGPT has proven valuable in creating Jupyter Notebooks that are both informative and engaging. The example notebooks in this repository have been generated with the help of ChatGPT, which has provided the code blocks, explanations, and descriptions for each notebook. By integrating ChatGPT within the notebook creation process, developers can generate content more efficiently, focus on their core ideas, and spend less time on the repetitive aspects of the process.
However, it is important to note that while ChatGPT has been immensely helpful in generating the content for this repository, human intervention is still necessary to ensure the highest quality. Developers need to review and polish the generated content, verify the accuracy of the information provided, and make any necessary adjustments to align with best practices and specific project requirements. As powerful as ChatGPT is, it remains a tool to assist developers rather than replacing the need for human expertise and judgment.
A company was having an issue with a complex software problem and couldn't find a solution. Desperate, they reached out to a retired engineer renowned for his expertise. The engineer agreed to help and, after analyzing the issue, he used ChatGPT to generate a solution, which he then applied to the software. Miraculously, the software started functioning perfectly again.
The company was relieved but was shocked when they received the engineer's bill, which was for $10,000. They requested an itemized invoice to justify the high cost. The retired engineer obliged and sent them the following itemized bill:
- Using ChatGPT to generate a solution: $5
- Knowing which prompt to give ChatGPT: $9,995
[100% of this README, aside from this, was generated by ChatGPT]