Summary Summarizing Papers With Python and GPT-3 | by Lucas Soares | Geek Culture | Medium medium.com
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One Line
Using Python, OpenAI's GPT-3 API, and various packages, a tool was created to summarize papers from their arxiv address, which also proved that DNNs have a better generalization ability when the loss function is flatter and when the DNN parameters are smaller.
Key Points
- Fourier analysis was used to understand the gradient-based optimization of deep neural networks (DNNs) on real data and pure noise.
- Experiments showed that DNNs have a better generalization ability when the loss function is flatter and when the DNN parameters are smaller.
- Using Python and OpenAI's GPT-3 API, a simple tool can be created to summarize papers directly from their arxiv address.
- Mobile First and optimized note-taking advice is available.
- Business concerns for Machine Learning, including help, terms, and privacy, are available via the Medium app.
Summary
262 word summary
Using Python and OpenAI's GPT-3 API, a simple tool can be created to summarize papers directly from their arxiv address. Steps to do so include downloading the paper, converting it from PDF to text, feeding the text to GPT-3 using the API, and displaying the summary. Python packages such as openai, wget, pdfplumber, and numpy can be used to do this. We study DNN training using Fourier analysis. Our framework explains why DNNs with (stochastic) gradient-based methods often achieve low generalization error, and how small initialization leads to good generalization ability of DNNs while preserving their ability to fit any function. Experiments of DNNs are used to confirm these results. Fourier analysis was used to understand the gradient-based optimization of deep neural networks (DNNs) on real data and pure noise. Results showed that DNNs have a better generalization ability when the loss function is flatter and when the DNN parameters are smaller. The code for this project is available on GitHub. This post is a proof of concept for summarization models, but further tweaking is needed for formatting and quality. A YouTube video and course on natural language processing for text summarization are available. Sign up to Geek Culture Hits to get the top 10 stories delivered to your inbox each week. Prerequisites for setting up geo-replication include two virtual machines. Mobile First and optimized note-taking advice is available. Also, explore Generators and Async Programming in Python and learn 5 tips for beginners investing in stocks. Business concerns for Machine Learning, including help, terms, and privacy, are available via the Medium app.