The latest issue of the digest is already waiting for you on our website! Tap on the link and feel free to subscribe 👇🏻
https://bit.ly/3sTKMj5

💡SSWL-IDN: Self-Supervised CT Denoising

In this article, Ayaan Hague provides an explanation of SSWL-IDN that leverages residual learning and a hybrid loss combining perceptual loss and MSE, all incorporated in a VAE framework.
https://bit.ly/3mzbs7P

​​We know that some of you are looking for job opportunities. Here is a list of 10 positions available this week, enjoy!
1) Machine Learning Engineer, Shelf
https://bit.ly/2WqvaaI
2) Data Scientist, Shelf
https://bit.ly/3zmOhRv
3) Senior/Middle CV/ML Engineer, Apostera
https://bit.ly/2Wuhyeq
4) Senior Data Scientist for Sport Stream, Parimatch Tech
https://bit.ly/38fPEpg
5) Data Scientist (NLP), SoftServe
https://bit.ly/3ks18vv


For other 5 positions click 👉🏻 https://bit.ly/3mKTtuW

Did you find something for yourself? Let us know!

📌How to Train a BERT Model From Scratch

A BERT 101. In this article, you'll find a step-by-step guide to training a functional model from scratch. All guidelines are clear and will work well for beginners.
https://bit.ly/3zpXWqC

💡DL-Traff: Survey and Benchmark of Deep Learning Models for Urban Traffic Prediction

In the paper, the authors review the deep traffic models and the widely used datasets, build a standard benchmark to evaluate their performances with the same settings and metrics.
https://bit.ly/3jpEhBt

📌Machine Learning Pipeline End-to-End Solution

ML implementations are supported by dozens of different services. Complexity is not necessarily a good thing. In this article, you'll learn how ML system can be split into as few services as possible.
https://bit.ly/3gGgJqs

📌Neural-GIF: Neural Generalized Implicit Functions for Animating People in Clothing

Neural Generalized Implicit Functions (Neural-GIF) is a method to animate people in clothing as a function of the body pose trained on various raw 3D scans.
https://bit.ly/3yBNUBG

The Data Phoenix Events team invites you all on September 8 to our "The A-Z of Data" webinar. The topic — deploying deep learning models with Kubernetes and Kubeflow.

In this talk, we'll learn about deploying Keras models. First, we'll see how to do it with TF-Serving and Kubernetes, and in the second part of the talk, we'll do it with KFServing and Kubeflow.

Speaker

Alexey Grigorev - Principal Data Scientist at OLX Group, Founder at DataTalks.Club. Alexey wrote a few books about machine learning. One of them is Machine Learning Bookcamp — a book for software engineers who want to get into machine learning.

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

💡Creating Synthetic Data for Machine Learning

This tutorial will guide you through the steps needed to create the synthetic data and show how you can then train it with YOLOv5 in order to work on real images.
https://bit.ly/3t1dQ8y

Are you onboard to receive our weekly newsletter? Fill in your email and get instant access to all the AI/ML goodies in one go. Looking forward to having you as one of our amazing subscribers!
https://bit.ly/3yyEt5K

The Data Phoenix Events team invites you all on September 16 to our "The A-Z of Data" webinars. The topic — re-usable pipelines for ML projects with DVC.

Good ML pipelines ensure reproducibility of ML experiments and controllability of the development process. In practice, there are often situations when you want to reuse the code of one project into a new one. Sometimes, a new project (model) differs only in the target variable. In such cases, you can reuse up to 95% of the developments from the previous project. This talk discusses the approaches to organize and configure ML pipelines using DVC, ways to reuse ML pipelines, and typical scenarios where this can come in handy.

Speaker

Rozhkov Mikhail - Solution Engineer at Iterative.ai. ML Engineer and enthusiast with over six years of experience in Machine Learning and Data Science. Co-creator ML REPA, author of courses on automating ML experiments with DVC and MLOps. As a member of the Iterative.ai team, he helps teams improve ML development and automate MLOps processes.

Participation is free, but pre-registration is required

https://bit.ly/3yuXs0Z

📌VIL-100: A New Dataset and A Baseline Model for Video Instance Lane Detection

In the paper, the authors propose a new baseline model, named multi-level memory aggregation network (MMA-Net), for video instance lane detection.
https://bit.ly/3gTgrfM

Hey friends! Data Phoenix is here and we want to tell you that the latest issue of the digest is already waiting for you on our website! Tap on the link and feel free to subscribe 👇🏻
https://bit.ly/3mWVxjC

💡Bootstrap a Modern Data Stack in 5 minutes with Terraform

The guide with all the details to walk you through setting up Airbyte, BigQuery, dbt, Metabase, and everything else you need to run a Modern Data Stack using Terraform.
https://bit.ly/3zLgVfA

Data Phoenix Events together with Autodoc and VITech invites you all on September 15 to the meet-up of Open Data Science community in Odessa. During which we will discuss how NLP has changed throughout the last 10 years. In addition to that, we will talk about the experience of involvement in ML competitions. If you can’t attend in person there’s no problem because we are going to be Live as well.
For more details and registration tap this link 👉🏻
https://bit.ly/2WOT6oE

​​We are aware that some of you are looking for job opportunities. We got you and here is a list of 10 positions available this week, enjoy!
1) Machine learning Engineer (middle/senior), Depositphotos, Kyiv, Remote
https://bit.ly/3taJmB7
2) ML Developer for Data Science Team (Python), Rakuten, Kyiv, Odesa, Remote
https://bit.ly/3jGcRHK
3) AI/ML Computer Vision Engineer, Xenoss, Kyiv, Kharkiv, Lviv, Odesa, Remote
https://bit.ly/3n1m9jt
4) Data Scientist (Advanced Analytics), SoftServe, Lviv, Kyiv, Poland
https://bit.ly/3n44jwn
5) Data Scientist III, Rackspace, Remote (United States)
https://bit.ly/3BGQ70C

For other 5 positions click 👉🏻https://bit.ly/2WOT3c4

Did you find something for yourself? Let us know!

📌Anomaly Detection with TensorFlow Probability and Vertex AI

In this article, you'll learn how Google's AI team uses an ML solution for anomaly detection on Vertex AI to automate these laborious processes of building time series models.
https://bit.ly/3yGIPYB