💡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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📌OpenAI releases OpenAI Codex, an advanced AI system that translates natural language to code. The release is available through their API in private beta.
https://bit.ly/3fVTc4f
https://bit.ly/3fVTc4f)

💡C. Light Technologies is to design and build the AI solution that spots changes in eye motion to detect the earliest stage of Alzheimer’s disease.
https://bit.ly/2VNHySe

Hey friends!👋🏻
The latest issue of the digest is already waiting for you on our website! New articles, papers, books, and more! Tap on the link and feel free to subscribe 👇🏻
https://bit.ly/3AwkJkw

📌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!

Continuous Integration and Continuous Deployment (CI/CD) Tools for Machine Learning

Continuous integration and continuous deployment are standard software development practices. Naturally, they're used in ML as well. Check out this article to learn how.
https://bit.ly/3jVF508

​​Good morning folks! Here's your dose of positivity for this Sunday!🤗
https://bit.ly/3AHUMi7

📌Companies could spend nearly $342 billion on AI software, hardware, and services in 2021. The spending is to rise to $500 billion by 2024.

https://bit.ly/37JFQ6L

💡Make a Rock-Solid ML Model Using Sklearn Pipeline

Most of data is useless unless you perform a decent amount of transformation and preprocessing. In this article, you'll learn how to use Sklearn to design and build robust ML models.

https://bit.ly/2W29jq9

#DataPhoenix #DataScience #MachineLearning #ArtificialIntelligence #AI #ML #Data

📌Containerizing Apache Hadoop Infrastructure at Uber

This article provides a summary of problems the Uber team faced when re-architecting the Hadoop deployment stack (Docker containers), and how they solved them along the way.
https://ubr.to/3yTuKrT

💡Data Movement in Netflix Studio via Data Mesh

Learn about Netflix's journey to a more efficient data movement using Data Mesh, to improve the pace of production and efficiency of global business operations using the most up-to-date information.
https://bit.ly/3yWUV0R

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https://bit.ly/37UvSzC

📌Observation of Time-Crystalline Eigenstate Order on a Quantum Processor

The authors demonstrate the characteristic spatiotemporal response of a DTC for generic initial states. A time-reversal protocol discriminates external decoherence from intrinsic thermalization and uses quantum typicality to circumvent the cost of densely sampling the eigenspectrum.
https://bit.ly/3CTIBRn

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/2W9Iss8

💡Make Money Using NFT + AI | GAN Image Generation

In this article, you'll see how to create new images using GAN, with a focus on generating art using Stylegan2-ADA. The goal is to create contemporary art via NFT and sell it via Opeansea.
https://bit.ly/2We6xhD

The Data Phoenix Events team invites you all on August 25 to the second of our series of "The A-Z of Data" webinars. The topic — Monitoring ML Models in Production.

The performance of machine learning models can decline over time - due to changes in data, business processes, or simply data loss or failures. To avoid the negative impact on business performance, it is crucial to detect such situations and take timely action - for example, by retraining the model. Due to this, monitoring of services based on machine learning needs to include additional metrics related to the model and data quality. In the course of the tutorial, Emeli Dral will demonstrate how the quality of a model can change over time, and how one can track and analyze the changes using open source tools.

Participation is free, but pre-registration is required.
https://bit.ly/3giNlq0