Demystifying Text Data with the unstructured Python Library | Saeed Esmaili

In the world of data, textual data stands out as being particularly complex. It doesn’t fall into neat rows and columns like numerical data does. As a side project, I’m in the process of developing my own personal AI assistant. The objective is to use the data within my notes and documents to answer my questions. The important benefit is all data processing will occure locally on my computer, ensuring that no documents are uploaded to the cloud, and my documents will remain private.

To handle such unstructured data, I’ve found the unstructured Python library to be extremely useful. It’s a flexible tool that works with various document formats, including Markdown, , XML, and HTML documents.

Demystifying Text Data with the unstructured Python Library — https://saeedesmaili.com/demystifying-text-data-with-the-unstructured-python-library/

AI Reading List 7/6/2023

What I’m reading today.

 

Configuring Jupyter Notebook in Windows Subsystem Linux (WSL2) | by Cristian Saavedra Desmoineaux | Towards Data Science

Here’s a great quick start guide to getting Jupyter Notebook and Lab up and running with the Miniconda environment in WSL2 running Ubuntu. When you’re finished walking through the steps you’ll have a great data science space up and running on your Windows machine.

I am going to explain how to configure Windows 10 and Miniconda to work with Notebooks using WSL2

Source: Configuring Jupyter Notebook in Windows Subsystem Linux (WSL2) | by Cristian Saavedra Desmoineaux | Towards Data Science

AI Reading List 7/5/2023

The longer holiday weekend  edition.