Update README.md

README.md

@@ -9,30 +9,12 @@ pinned: false
 
 <img src="https://www.datawaza.com/en/latest/_static/datawaza_logo_name_trans.svg" alt="datawaza_logo_name_trans.svg" width="300"/>
 
-[Datawaza](https://github.com/jbeno/datawaza) streamlines common Data Science tasks. It's a collection of tools for data exploration, visualization, data cleaning, pipeline creation, hyper-parameter searching, model iteration, and evaluation. It builds upon core libraries like [Pandas](https://pandas.pydata.org/), [Matplotlib](https://matplotlib.org/), [Seaborn](https://seaborn.pydata.org/), and [Scikit-Learn](https://scikit-learn.org/stable/).
+[Datawaza](http://www.datawaza.com) streamlines common Data Science tasks. It's a collection of tools for data exploration, visualization, data cleaning, pipeline creation, hyper-parameter searching, model iteration, and evaluation. It builds upon core libraries like [Pandas](https://pandas.pydata.org/), [Matplotlib](https://matplotlib.org/), [Seaborn](https://seaborn.pydata.org/), [Scikit-Learn](https://scikit-learn.org/stable/), [TensorFlow](https://www.tensorflow.org), and [PyTorch](https://pytorch.org).
 
-## Installation
+## Open Source Library
 
-The [latest releases](https://pypi.org/project/datawaza/) can be found on PyPi. Install Datawaza with pip::
-
-```
-pip install datawaza
-```
-
-See the [Change Log](https://github.com/jbeno/datawaza/blob/main/CHANGELOG.md>) for a history of changes.
-
-## User Guide
-
-[User Guide](https://www.datawaza.com/en/latest/userguide.html) is a Jupyter notebook that walks through how to use the Datawaza functions. It's probably the best place to start, and then you can reference the function specs organized by module above.
-
-## Source Code
-
-You can find the [Datawaza repo](https://github.com/jbeno/datawaza/) on Github. Please submit any issues there. It's distributed under the GNU General Public License. Contributions are welcome!
+You can find the [Datawaza repo](https://github.com/jbeno/datawaza/) on Github, and the [latest release](https://pypi.org/project/datawaza/) on PyPi. The [user guide](https://www.datawaza.com/en/latest/userguide.html) is a Jupyter notebook that walks through how to use the Datawaza functions. It's probably the best place to start.
 
 ## What is Waza?
 
-Waza (技) means "technique" in Japanese. In martial arts like Aikido, it is paired with words like "suwari-waza" (sitting techniques) or "kaeshi-waza" (reversal techniques). So we've paired it with "data" to represent Data Science techniques: データ技 "data-waza".
-
-## Origin Story
-
-Most of these functions were created while I was pusuring a [Professional Certificate in Machine Learning & Artificial Intelligence](https://em-executive.berkeley.edu/professional-certificate-machine-learning-artificial-intelligence) from U.C. Berkeley. With every assignment, I tried to simplify repetitive tasks and streamline my workflow. They served me well, so I'm publishing this library in the hope that it may help others.
+Waza (技) means "technique" in Japanese. In martial arts like Aikido, it is paired with words like "suwari-waza" (sitting techniques) or "kaeshi-waza" (reversal techniques). So we've paired it with "data" to represent Data Science techniques: データ技 "data-waza".