π¨ BREAKING: IBM launches a free Python library that converts ANY document to data
Introducing Docling. Here's what you need to know: π§΅
1. What is Docling?
Docling is a Python library that simplifies document processing, parsing diverse formats β including advanced PDF understanding β and providing seamless integrations with the gen AI ecosystem.
2. Document Conversion Architecture
For each document format, the document converter knows which format-specific backend to employ for parsing the document and which pipeline to use for orchestrating the execution, along with any relevant options.
3. PDF Conversion to Markdown
Here is an example of the DocLayNet paper from arXiv, converted into Markdown format by Docling.
4. Core Technology:
Docling includes:
- PDF Backends for parsing
- Layout Analysis Model
- Vision-Based Table Formatter
- OCR for Text
5. Every data analyst, data scientist, and data engineer needs to learn Generative AI
99% of them are overlooking AI. This is a massive opportunity for you.
I'd like to help.
On Wednesday, August 20th, I'm sharing one of my best AI/ML Projects for FREE:
How I built an AI Customer Segmentation Agent with Python:
Type 1 and Type 2 errors are confusing. In 3 minutes, I'll demolish your confusion. Let's dive in. π§΅
1. Type 1 Error (False Positive):
This occurs when the pregnancy test tells Tom, the man, that he is pregnant. Obviously, Tom cannot be pregnant, so this result is a false alarm. In statistical terms, it's detecting an effect (in this case, pregnancy) when it actually doesn't exist.
2. Type 2 Error (False Negative):
This happens when Lisa, who is actually pregnant, takes the test, and it tells her that she's not pregnant. The test failed to detect the real condition of pregnancy. In statistical terms, it's failing to detect a real effect (pregnancy) that is there.
Boxplots are one of the most useful tools in my Data Science arsenal.
In 6 minutes, I'll eviscerate your confusion.
Let's dive in.
1. What is a boxplot?
A boxplot is a standardized way of displaying the distribution of data based on a five-number summary: minimum, first quartile (Q1), median, third quartile (Q3), and maximum.
2. Invention:
The boxplot was invented in 1969 by John Tukey, as part of his pioneering work in data visualization. Tukey's EDA emphasized the importance of using simple graphical and numerical methods to start understanding the data before making any assumptions about its underlying distribution or applying complex statistical models. The boxplot emerged from this philosophy. Tukey's boxplot was designed to be a quick and easy way to visualize the distribution of data.