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!
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!
💡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)
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)
Emily Riederer
A lightweight data validation ecosystem with R, GitHub, and Slack | Emily Riederer
A right-sized solution to automated data monitoring, alerting, and reporting using R (`pointblank`, `projmgr`), GitHub (Actions, Pages, issues), and Slack
📌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)
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)
💡ML Metadata Store: What It Is, Why It Matters, and How to Implement It
Learn about ML metadata stores, how they are different from other tools used for building models, and how they can help you build and deploy models with more confidence.
https://bit.ly/3Cz3Tmq
https://bit.ly/3Cz3Tmq)
Learn about ML metadata stores, how they are different from other tools used for building models, and how they can help you build and deploy models with more confidence.
https://bit.ly/3Cz3Tmq
https://bit.ly/3Cz3Tmq)
neptune.ai
ML Metadata Store: What It Is, Why It Matters, How to Implement It
Learn about ML metadata stores, their importance, and how to set one up, focusing on practical management techniques.
📚Learn Data Science with R
This book for data science beginners covers statistics, R, graphing, and machine learning. It features many practical examples that will help you put theory into practice.
https://bit.ly/3kr6HM2
https://bit.ly/3kr6HM2)
This book for data science beginners covers statistics, R, graphing, and machine learning. It features many practical examples that will help you put theory into practice.
https://bit.ly/3kr6HM2
https://bit.ly/3kr6HM2)
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)
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)
📢 Мы в эфире. Начинаем вебинар "From research to product with Hydrosphere", на котором поговорим про превращение ML исследования в продукт с использованием Hydrosphere. Присоединяйтесь - https://bit.ly/3hZASYW
YouTube
Webinar "From research to product with Hydrosphere"
Пятый технический вебинар из серии "The A-Z of Data", который посвящен превращению ML исследования в продукт с использованием Hydrosphere.https://dataphoenix...
📌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)
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)
Great news! Data Phoenix just published the latest issue of the digest! And it is already waiting for you on our website! Tap on the link and feel free to subscribe 👇🏻
https://bit.ly/3zBY7hE
https://bit.ly/3zBY7hE
Data Phoenix
Data Phoenix Digest - 23.09.2021
CV for workplace security, a new wave of invest into NLP, webinars "Pachyderm in production: lessons learned", fast AutoML with FLAML + Ray Tune, improving neural network subspaces, YOLOv5 on CPUs, learning neural causal models with active interventions,…
💡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
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
Medium
GPT-4 Will Have 100 Trillion Parameters — 500x the Size of GPT-3
Are there any limits to large neural networks?
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
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
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!
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!
💡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
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
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
💡Supercharge Image Classification with Transfer Learning
Hop in to learn how to leverage pretrained ResNets from Tensorflow-Hub to take advantage of their ability to be easily transfer-learnt/fine-tuned on new datasets.
https://bit.ly/2XYcQXN
Hop in to learn how to leverage pretrained ResNets from Tensorflow-Hub to take advantage of their ability to be easily transfer-learnt/fine-tuned on new datasets.
https://bit.ly/2XYcQXN
Towards Data Science
Supercharge Image Classification with Transfer Learning | Towards Data Science
Image Classification using pretrained ResNets and BiT Models
Hey folks!
Don't forget that tomorrow we have our next webinar "Pachyderm in production: lessons learned"
Our speaker is Oleh Lokshyn is a Machine Learning Architect at SoftServe. He built ML workflows on GCP, Azure, and on-premises for different supervised and unsupervised models. Oleh holds several certifications: Google Cloud Professional Machine Learning Engineer, Google Cloud Professional Data Engineer, Microsoft Certified Azure Data Scientist Associate.
For more info and registration tap 👉🏻 https://bit.ly/3kMUxNF
Don't forget that tomorrow we have our next webinar "Pachyderm in production: lessons learned"
Our speaker is Oleh Lokshyn is a Machine Learning Architect at SoftServe. He built ML workflows on GCP, Azure, and on-premises for different supervised and unsupervised models. Oleh holds several certifications: Google Cloud Professional Machine Learning Engineer, Google Cloud Professional Data Engineer, Microsoft Certified Azure Data Scientist Associate.
For more info and registration tap 👉🏻 https://bit.ly/3kMUxNF
Data Phoenix
Webinar "Pachyderm in production: lessons learned" (RU)
In this talk, we will take a look at yet another MLOps tool - Pachyderm. This tool is gaining in popularity and is unique for some use-cases. The speaker will share the experience of applying Pachyderm to a real-world, BigData NLP project. Most importantly…
📌Dual-Camera Super-Resolution with Aligned Attention Modules
The paper presents a novel approach to reference-based super-resolution with the focus on dual-camera super-resolution, which utilizes reference images for high-quality and high-fidelity results.
https://bit.ly/3um1ubw
The paper presents a novel approach to reference-based super-resolution with the focus on dual-camera super-resolution, which utilizes reference images for high-quality and high-fidelity results.
https://bit.ly/3um1ubw
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https://bit.ly/3A2Qkdj
https://bit.ly/3A2Qkdj
Data Phoenix
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.