The Austin Python Meetup Monthly Meetup

We typically have a main presentation or a series of lightning talks, followed by discussion and Q&A. There is a diversity of domains and experience levels represented, so come with your questions and be prepared to talk about how you use Python!

This will be an online meeting - please join the meetup at the link listed. Please note that this link may update and updates may appear in the discussion section below - so scroll down if you have technical difficulties.

The presentations will start after 7, yet feel free to join starting 6:30.

In this meetup we will have the following presentations:

Talk 1: Yong Tang will talk about "Machine Learning Data Preparation and Augmentation with TensorFlow"

Talk 2: Gunnar Kleemann will talk about "Digging into Public Cancer Literature Using Python"

Details about the presentations below:

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Talk 1: Yong Tang "Machine Learning Data Preparation and Augmentation with TensorFlow"

One of the biggest challenges in machine learning is the preparation and augmentation of data. While the model formats in machine learning have been largely standardized, there is a great variety of data input sources that almost always require special processing in preparation and augmentation. In this talk, we will discuss tensorflow and tensorflow-io, widely used python packages for machine learning & data processing. We will guide through several python examples to demonstrate data preparation and augmentation for machine learning algorithms.

Yong Tang is Director of Engineering at MobileIron. He contributes to many projects for the open-source community. He is a maintainer and SIG I/O lead of the TensorFlow project, with a focus on data processing in machine learning. In addition to TensorFlow, Yong Tang is also a maintainer of widely used Docker project, and a maintainer of CNCF-graduated CoreDNS project in cloud-native & container ecosystem.

Talk 2: Gunnar Kleemann "Digging into Public Cancer Literature Using Python"

Data originating from deep-tech domains such as biotechnology, agriculture, and chemistry often require significant study to understand. At the same time, deep-tech data is becoming more relevant and available in the form of publicly funded data repositories. Cancer research is an example of a deep-tech treasure that can be accessed with the right tools. In this talk, I will demonstrate how I used Biopython for the collection and compilation of cancer data and then the Grakn python client to analyze it with a knowledge graph. This pipeline allowed me to get a birds-eye view of multiple cancer types and the research groups working on them.

Gunnar is the Principal Data Scientist and owner of the Capital Data Corp of Austin (ACD) as well as the co-founder of the Berkeley Data Science Group. He is interested in how data science facilitates biological discovery and lowers the barrier to high-throughput research, particularly in small, independent labs. One of his passions is enabling scientific research through teaching, mentorship. In line with this goal, he offers consulting services to local businesses, teaches data science for the UC Berkeley School of Information, and runs pythonformakers.org.