A special review of the GPT models by OpenAI for certain tasks

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Create a python package with poetry in production

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The more you use it, the more you love it

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Living in a competitive world, it is very important to understand the pros and cons of competition. And more importantly, when to compete and when not to compete.

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It appears to me that if one wants to make progress in mathematics one should study the masters and not the pupils - N.H. Abel

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Outside of the Python community, there are many other communities that are worth to explore, such as the Typescript community.

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According to Warren Buffett, "you only know who is swimming naked when the tide goes out." The same is true for AI. The current AI tide is high, but what will happen when the tide goes out? In this post, I will share some of my thoughts on the future of AI.

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Manim could be used to make videos for non-mathematical purposes as well.

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The title tells it all and transformers are here to stay. If you want to join the trend of shaping the future of NLP, this is the place to start.

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A quick guide on how to do topic modeling with Python

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According to John Chambers, everything that exists is an object in R and everything that happens is a function call. This summarizes the philosophy of R. Knowing this, we can understand R better.

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This is my first quarter review for 2023, which reviews my progress on my goals for the year and share some of my thoughts on the quarter.

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There are many packages in R or Python to deal with table like data (or so called data frame), but data.table is probably the most efficient one. Here are some best practices to use data.table.

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Not many models balance complexity and effectiveness as well as LDA. I like this model so much as it is perhaps the best model to start with when you want to learn about machine learning and deep learning models. Why? I will explain in this post.

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There are many projects in the European Union that are funded by the European Research and Innovation Programme. To make sense of those projects and their results, we have to do lots of data processing and analysis.

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Training and tuning a deep learning model is a complex process. This post will cover the basics of how to initialize the parameters of a deep learning model.

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As key components of neural networks, activation functions are responsible for transforming the input data into the desired output. In this article, we will discuss the most common activation functions and their applications.

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A guide to building a full stack deep learning application with PyTorch in a small scale, from data collection to model saving without deploying to production.

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Embracing the python and R community together needs smooth transference of our mentalities and skills from both community; I believe one could get best of both worlds in VS Code.

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It took me a while to get into patent analytics, which is a 'wild' world. I hope my guide for patent analytics could help you to navigate through.

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What are the best tools to write and publish scientific or research blogs? I end up using Jekyll with Katex rendering on server-side.

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The legal system for patenting is not very straightforward with all those legal terms; things become easier with ChatGPT

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What should we do when we realize some packages from Python or R could not be installed in our local machine?

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Based on the framework of 3brown1blue, I tried to make some animation for mathematics.

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Sometimes, you might need Stata

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I used Mkdocs to build my webiste for a while before switching to Jekyll.

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After scraped a fair amount of websites, I wrote down what I have learned.

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Quick guide on how to create a virtual environment in Python

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Here is some extra detail about the post.

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