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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: @dmitryspodarets
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​​Hi friends! The Data Phoenix team invites you all August 17 to the first of our series of MLOps webinars ennoscriptd "The A-Z of Data" During the pilot webinar — "The A-Z of Data: Introduction to MLOps" — we will explore what MLOps is, MLOps principles and best practices, major tools for MLOps implementation, and several architecture implementations. We will start with a basic ML lifecycle and move forward to best practices of building complicated, fully automated MLOps pipelines.

Speaker

Dmitry Spodarets — Head of R&D at VITech; active participant of the Open Data Science community; AWS Competency in Machine Learning.

"The A-Z of Data"— A series of webinars from Data Phoenix Events designed to help data scientists, data engineers, and all interested in data to expand the horizons of their AI/data expertise.

Stay tuned! It will be fun!
https://bit.ly/3jqh8NY
Natural Language Processing [Huggingface Course] 📚
During this course, you'll learn the basics of NLP using libraries from the Hugging Face ecosystem — Transformers, Datasets, Tokenizers, and Accelerate — as well as the Hugging Face Hub.
https://bit.ly/3CsFcZA
Data Phoenix pinned «​​Hi friends! The Data Phoenix team invites you all August 17 to the first of our series of MLOps webinars ennoscriptd "The A-Z of Data" During the pilot webinar — "The A-Z of Data: Introduction to MLOps" — we will explore what MLOps is, MLOps principles and…»
💡Elastic Graph Neural Networks

In this paper, the authors introduce a family of GNNs (Elastic GNNs) based on ℓ1 and ℓ2-based graph smoothing and propose a novel and general message passing scheme into GNNs. Experiments demonstrate that Elastic GNNs obtain better adaptivity on benchmark datasets.
https://bit.ly/3lBHYWj
​​Hey folks!

As you might already know, Data Phoenix is now offering you all a unique opportunity to host and promote your content at our website and in the digest itself. We're looking for original submissions and contributions, but we also consider reposts.

So, if you have any content that's truly awesome and that's worth sharing with the community, kindly reach out to us at editor@dataphoenix.info to discuss how it's going to work.
We know that some of you are looking for job opportunities now. We've put together a list of 10 amazing positions available this week. Stay tuned!

1) Computer Vision Engineer, SoftServe
https://bit.ly/3AgsJWL
2) Data Scientist (Advanced Analytics), SoftServe
https://bit.ly/2TWkijW
3) Data Engineer, Fintech Solutions, DataArt
https://bit.ly/3itAcvE
4) Senior IT Data Analyst, Raiffeisen Bank
https://bit.ly/3rVsjSO
5) R&D CV/ML Engineer, Augmented Pixels
https://bit.ly/3AkaVtM

For other 5 positions click 👉🏻 https://bit.ly/3Aklzkf
📌Image Super-Resolution via Iterative Refinement

Chitwan Saharia et al. present SR3, an approach to image Super-Resolution via Repeated Refinement. SR3 adapts denoising diffusion probabilistic models to conditional image generation and performs super-resolution through a stochastic denoising process.
https://bit.ly/3AiBdN3
​​Good morning people! It's Sunday and the best way to start your day is to smile!🤗
💡SynLiDAR: Learning From Synthetic LiDAR Sequential Point Cloud for Semantic Segmentation

SynLiDAR is a synthetic LiDAR point cloud dataset that contains large-scale point-wise annotated point cloud with accurate geometric shapes and comprehensive semantic classes, which the authors used to design PCT-Net, to narrow down the gap with real-world point cloud data.
https://bit.ly/3CvT5WC
📚Designing, Visualizing and Understanding Deep Neural Networks

A collection of lectures on Deep Learning delivered by Sergey Levine at UC Berkeley in 2020/21. In total, the course features 66 lectures, from the ML basics to policy gradients and meta learning.
https://bit.ly/3fKyEMd
📌Alias-Free Generative Adversarial Networks

The synthesis process of typical generative adversarial networks depends on absolute pixel coordinates in an unhealthy manner. The authors trace its root cause and derive architectural changes that guarantee that unwanted information cannot leak into hierarchical synthesis.
https://bit.ly/3ix7E4v
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📌Building Architectures that Can Handle the World’s Data

Perceiver is a general-purpose architecture that can process data including images, point clouds, audio, video, and their combinations. Learn more about this universal architecture!

https://bit.ly/2VLxCc2
😊 Салют!
🙊 Бывает, что о важной, полезной конференции узнаешь уже по фотографиям с мероприятия, выложенных в сеть докладах и восторженных статусах коллег.
🔥 Есть способ не пропускать актуальные ивенты, загодя планировать время и бюджет на обучение.
🚀 Представляем канал наших друзей @gde_konfa, который поможет вам быть в курсе всех интересных конференций по data science, project/product менеджменту, маркетингу в Украине и не только! А теперь еще и много полезного online-контента: онлайн-курсы, конференциях и обучающие материалы.
⚠️ А еще, в канале часто публикуются уникальные промо-коды на ивенты.
​​We are aware that some of you are looking for job opportunities. We've put together a list of 10 positions available this week, enjoy!

1) Machine Learning Optimization Engineer, Data Science UA
https://bit.ly/3m4TgCD
2) Deep Learning Engineer, Reface
https://bit.ly/3yJo4MV
3) Data Scientist (Advanced Analytics), SoftServe
https://bit.ly/37Cu7Hd
4) Lead MLOps Engineer, SoftServe
https://bit.ly/3fWst7G
5) AI/ML Computer Vision Engineer, Xenoss
https://bit.ly/3lWiFOV
For other 5 positions click 👉🏻 https://bit.ly/3CKoR2h

Did you find something for yourself? Let us know!
​​Good morning folks! Here's your dose of positivity for this Sunday!🤗
https://bit.ly/3AHUMi7