Data Phoenix
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Data Phoenix is your best friend in learning and growing in the data world!
We publish digest, organize events and help expand the frontiers of your knowledge in ML, CV, NLP, and other aspects of AI. Idea and implementation: @dspodarets
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The Data Phoenix Events team invites you all on September 22 to our "The A-Z of Data" webinars.
The topic — From Research to Product with Hydrosphere.

With our speaker Andrii Latysh - Technical Product Owner in ML/DS at Provectus; Founder & Coordinator at Odyssey - Odessa Data Science Community; Machine Learning/Data Science Engineer and Consultant; Lecturer; Speaker; PhD student.

For more info and registration tap the link 👉🏻
https://bit.ly/3zpkK9i
Hey guys! Sorry for the short delay with this week's issue of the digest. We're focused on webinars right now, just to put out higher-quality content for you. Thank you for attending the webinars, by the way! But now it’s time to read and enjoy the digest!

In addition, we'd like to remind you that we'll have several events soon:

1) September 22 - Webinar "From research to the product with Hydrosphere"
2) September 29 - Webinar "Pachyderm in production: lessons learned"

Kindly register to listen to our awesome speakers; we'd love to see you anytime!

NOTE: If you missed any of our previous webinars, they're available on our YouTube channel. Please, take a look and make sure to comment. With love from Data Phoenix Team!
https://bit.ly/2Z1d3t7
https://bit.ly/3kj70sn)
📌From Contexts to Locality: Ultra-high Resolution Image Segmentation via Locality-aware Contextual Correlation

The authors innovate the widely used high-resolution image segmentation pipeline, in which an ultra-high resolution image is partitioned into regular patches for local segmentation and then the local results are merged into a high-resolution semantic mask.
https://bit.ly/2XpK5T1
https://bit.ly/2XpK5T1)
​​Here comes a list of 10 positions available this week, enjoy!
1) Senior/Lead ML Engineer - Data Science UA, Kyiv, Remote
https://bit.ly/3kln8tm
2) Deep Learning Engineer - Reface, Kyiv, Remote
https://bit.ly/2Z4p0ON
3) Data Science Engineer - Deloitte, Kyiv, Remote
https://bit.ly/3ArxBcc
4) Senior Data Engineer - Lohika, Odesa, Remote https://bit.ly/3Corxlf
5) ML Engineer - Scalarr, Kyiv, Kharkiv, Ukraine (Remote) https://bit.ly/3Cglsr8

For the other 5 positions click 👉🏻 https://bit.ly/39gWZFx

Looking to feature your open positions in the digest? Kindly reach out to us at editor@dataphoenix.info for details. We'll be proud to help your business thrive!
​​Good morning friends! Here's your dose of positivity for this Sunday!🤗
https://bit.ly/3zltGfF
💡A Lightweight Data Validation Ecosystem with R, GitHub, and Slack

Data quality monitoring is an essential part of any data analysis or business intelligence workflow. In this article, you'll learn how to build a data validation system with at-hand tools.
https://bit.ly/3CtHvea
https://bit.ly/3CtHvea)
📌Parsing Table Structures in the Wild

This paper tackles the problem of table structure parsing from images in the wild. It establishes a practical table structure parsing system for scenarios where tabular input images are taken or scanned with severe deformation, bending, or occlusions.
https://bit.ly/2XIrjH5
https://bit.ly/2XIrjH5)
Hi friends!
Don't forget that today we are going to have our event with a topic — "From Research to Product with Hydrosphere"
And our speaker is Andrii Latysh - Technical Product Owner in ML/DS at Provectus; Founder & Coordinator at Odyssey - Odessa Data Science Community; Machine Learning/Data Science Engineer and Consultant; Lecturer; Speaker; PhD student.

Participation is free, but pre-registration is required.
https://bit.ly/3EIff9i

See you in a while today at 19:00 GMT+3
https://bit.ly/3AAIf0j)
📌GeneAnnotator: A Semi-automatic Annotation Tool for Visual Scene Graph

GeneAnnotator is a semi-automatic scene graph annotation tool for images that allows human annotators to describe the existing relationships in the visual scene in the form of directed graphs.
https://bit.ly/3kw8WxF
https://bit.ly/3kw8WxF)
💡GPT-4 Will Have 100 Trillion Parameters — 500x the Size of GPT-3

In this overview article, you'll learn about the potential (and limits) of GPT-4, an autoregressive language model that is designed to outperform GPT-3. Maybe be released next year!

https://bit.ly/3o6YSxc
Hey friends!
We would like to remind you about our YouTube channel where you can find all the footage from our latest events. Data Phoenix team understands that there are special circumstances that can make you miss our live event. That's why we uploaded all the videos online so you can watch them later and don't miss a thing! Tap the link and enjoy!
https://bit.ly/2XHYGJF
📌LightAutoML: AutoML Solution for a Large Financial Services Ecosystem

LightAutoML is an AutoML system developed for a large European financial services company and that has already been deployed in numerous applications. The paper presents an overview of it.

https://bit.ly/3zF22uq
​​We are aware that some of you are looking for job opportunities. Here is a small list of positions available this week, enjoy!
1) Computational Materials Scientist AI/ML – Exabyte.io , San Francisco, Remote
https://bit.ly/3EUacTc
2) Junior Data Engineer – MoonPay, Remote (Europe)
https://bit.ly/3ENSMb0
3) Data Scientist – CB Insights, New York or Remote
https://bit.ly/3lSrkQK
4) Principal Software Engineer – Machine Learning – Twilio, Remote(US)
https://bit.ly/3uckBoy
5) Senior Data Engineer – 1Password, Remote (US or Canada)
https://bit.ly/39EM5cS

Did you find something for yourself? Let us know!

Looking to feature your open positions in the digest? Kindly reach out to us at editor@dataphoenix.info for details. We'll be proud to help your business thrive!
​​Good morning folks! Here's your dose of positivity for this Sunday!🤗
https://bit.ly/39DhdJP
💡How to Create an AutoML Pipeline Optimization Sandbox

In this article, we'll look into the ways and methods of implementing an automated machine learning pipeline optimization sandbox web app using Streamlit and TPOT.
https://bit.ly/39CiCR1
📌Revisiting 3D ResNets for Video Recognition

In this paper, the researchers explore training and scaling strategies for video recognition models and propose a simple scaling strategy for 3D ResNets.

https://bit.ly/2ZzeSxU