PyLadies June Meetup - It´s summer time!

PyLadies June Meetup:

🌴😎🍍 It´s summer time 🌴😎🍍
Drinks, talks, and a lot of networking in the charming event space of Netlight ✨. Join the RSVP to attend the event in person. It's summertime!

Make sure to provide your First and Last Names for a check at the event entrance.

❗❗❗ The meetup space is on Netlight ⭐ on the 5th floor, and you can use the elevator to reach the event space ❗❗❗

🍍 Plan!!! 🍍

👉 18:00 Warm welcome, food, drinks, chat

👉 18:30: Anna Polizzotto: How to DocumentAI - Document Understanding Models in Action

Through the automatic processing of internal and external data, companies can gain precious business insights and speed up processes. Though, such data is often unstructured and can come in many forms and shapes - tables, diagrams and invoices are only some of the possible formats featured in these datasets. Due to this heterogeneity, unlocking the potential of a company's document pool is no trivial task.

Here, Document Understanding Models can come to the rescue by combining visual and textual inputs for executing DocumentAI tasks like document classification, visual question answering and form understanding, among others. In this session we will look at the pros and cons of such models, as well as we will go through a concrete use-case to showcase their power and their limitations.

👉 19:00 Furkan M. Torun: How to Build an Open-Source Machine Learning Platform in Biology?

Healthcare is moving towards real personalized medicine, for which complex and large biological datasets called “—omics” (e.g., genomics and proteomics) are crucial to guide medical intervention. However, these data are so extensive that support for interpreting health and disease states is needed. Although machine learning (ML) has become an indispensable tool for this goal, it is sometimes applied in an opaque and unreproducible manner without applying the best practices of ML. To address issues like reproducibility or transparency and to grant researchers access to ML for their omics datasets without any programming or bioinformatics skills, we developed “OmicLearn” (OmicLearn.org), an open-source, web-based, easy-to-use ML platform.

OmicLearn is tailored to the needs of researchers in the biology and omics fields. It also fosters open and reproducible science via transparent assessment of state-of-the-art algorithms in a standardized format. This talk is for every scientist and developer who is interested in biology or omics or who wants to learn how to build a machine learning platform from open-source tools.

- Source code: OmicLearn.org
- Article published in Journal of Proteome Research: https://doi.org/10.1021/acs.jproteome.2c00473

👉 19:30: Raffle of prizes for our lucky winners
01 Book - Architecture Patterns with Python by Harry Percival, Bob Gregory thanks to Cloudflight for sponsoring this prize.
02 Subscription for Jetbrains product like PyCharm

See you!