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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.
A best channel for AI and Python projects with source code.
Join to channel 🌺🌺
https://news.1rj.ru/str/CodeProgrammer
Join to channel 🌺🌺
https://news.1rj.ru/str/CodeProgrammer
Telegram
Machine Learning with Python
Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers.
Admin: @HusseinSheikho || @Hussein_Sheikho
Admin: @HusseinSheikho || @Hussein_Sheikho
TextOCR
TextOCR is a dataset to benchmark text recognition on arbitrary shaped scene-text that features 1M high-quality word annotations on TextVQA images. It allows data scientists to more easily apply end-to-end reasoning to downstream tasks, such as visual question answering or image captioning.
https://bit.ly/37890MA
TextOCR is a dataset to benchmark text recognition on arbitrary shaped scene-text that features 1M high-quality word annotations on TextVQA images. It allows data scientists to more easily apply end-to-end reasoning to downstream tasks, such as visual question answering or image captioning.
https://bit.ly/37890MA
9 августа стартует онлайн-интенсив «Machine Learning. Введение в регрессионный анализ».
За 3 недели разработчики, аналитики и другие специалисты со знанием синтаксиса Python научатся решать задачи по прогнозированию с помощью трех методов и построят первую ML-модель.
https://bit.ly/2UYMzHb
За 3 недели разработчики, аналитики и другие специалисты со знанием синтаксиса Python научатся решать задачи по прогнозированию с помощью трех методов и построят первую ML-модель.
https://bit.ly/2UYMzHb
Why Data Science Is the Future?
Data scientists have been on the radar generating tons of buzz lately. They are badly needed on the market, and the profession itself has immense potential, both money- and contribution-wise. As a data scientist, you can change the world for real, though all you do is work with data. Amazing!
Here are six reasons why data science is the ideal career for the future.
1. Companies Struggle to Manage Their Data
Businesses now have the tools to collect tons of data, but who's going to process and analyze it? Here's when data scientists enter the picture!
2. New Data Privacy Regulations Increase the Need for Data Scientists
In May 2018, the General Data Protection Regulation (GDPR) took effect for countries in the European Union. In 2020, California enacted a similar regulation for data privacy. The GDPR increased the reliance companies have on data scientists, because now they have to focus more on managing and storing their data responsibly.
3. Data Science Is Still Evolving
Careers without growth potential stay stagnant, usually indicating that jobs within those respective fields must drastically change to remain relevant. Data science appears to have abundant opportunities to evolve over the next decade or so. Since it shows no signs of slowing down, that’s good news for people wanting to enter the field.
4. Data Scientists Have In-Demand Skills
Research shows 94 percent of data science graduates have got jobs in the field since 2011. One of the indicators that data science is well-suited for the future is the dramatic increase in data science job postings. Statistics from Indeed.com show a steady increase in the number of data science jobs listed over the years.
5. A Staggering Amount of Data Growth
People generate data daily, but most probably don’t even think about it. According to a study about current and future data growth, 5 billion consumers take part in data interactions daily, and that number will increase to 6 billion by 2025, representing three-quarters of the world’s population.
6. High Likelihood of Career Advancement Opportunities
LinkedIn has recently picked data scientist as its most promising career. One of the reasons it got the top spot was that the average salary in the role — $130,000. LinkedIn’s study also considered the fact that data scientists could get easily promoted, giving the role a career advancement score of nine out of 10.
Data scientists have been on the radar generating tons of buzz lately. They are badly needed on the market, and the profession itself has immense potential, both money- and contribution-wise. As a data scientist, you can change the world for real, though all you do is work with data. Amazing!
Here are six reasons why data science is the ideal career for the future.
1. Companies Struggle to Manage Their Data
Businesses now have the tools to collect tons of data, but who's going to process and analyze it? Here's when data scientists enter the picture!
2. New Data Privacy Regulations Increase the Need for Data Scientists
In May 2018, the General Data Protection Regulation (GDPR) took effect for countries in the European Union. In 2020, California enacted a similar regulation for data privacy. The GDPR increased the reliance companies have on data scientists, because now they have to focus more on managing and storing their data responsibly.
3. Data Science Is Still Evolving
Careers without growth potential stay stagnant, usually indicating that jobs within those respective fields must drastically change to remain relevant. Data science appears to have abundant opportunities to evolve over the next decade or so. Since it shows no signs of slowing down, that’s good news for people wanting to enter the field.
4. Data Scientists Have In-Demand Skills
Research shows 94 percent of data science graduates have got jobs in the field since 2011. One of the indicators that data science is well-suited for the future is the dramatic increase in data science job postings. Statistics from Indeed.com show a steady increase in the number of data science jobs listed over the years.
5. A Staggering Amount of Data Growth
People generate data daily, but most probably don’t even think about it. According to a study about current and future data growth, 5 billion consumers take part in data interactions daily, and that number will increase to 6 billion by 2025, representing three-quarters of the world’s population.
6. High Likelihood of Career Advancement Opportunities
LinkedIn has recently picked data scientist as its most promising career. One of the reasons it got the top spot was that the average salary in the role — $130,000. LinkedIn’s study also considered the fact that data scientists could get easily promoted, giving the role a career advancement score of nine out of 10.
Have you received our weekly newsletter? Not yet? Well, no need to wait! 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!
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#DataPhoenix #DataScience #MachineLearning #ArtificialIntelligence #AI #ML #Data #Digest #Newsletter
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#DataPhoenix #DataScience #MachineLearning #ArtificialIntelligence #AI #ML #Data #Digest #Newsletter
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.
Project CodeNet
Project CodeNet provides the AI-for-Code research community with a large scale, diverse, and high quality curated dataset to drive innovation in AI techniques. Check out their GitHub page to find the dataset you need.
https://bit.ly/3zRd28l
Project CodeNet provides the AI-for-Code research community with a large scale, diverse, and high quality curated dataset to drive innovation in AI techniques. Check out their GitHub page to find the dataset you need.
https://bit.ly/3zRd28l
GitHub
GitHub - IBM/Project_CodeNet: This repository is to support contributions for tools for the Project CodeNet dataset hosted in DAX
This repository is to support contributions for tools for the Project CodeNet dataset hosted in DAX - GitHub - IBM/Project_CodeNet: This repository is to support contributions for tools for the Pro...
Hello friends! Data Phoenix team know that some of you are looking for job opportunities now. As your caring friend, we've put together a list of 11 amazing positions available this week Let us know what you think!
1) Machine Learning Engineer, Shelf
https://bit.ly/3icOAIq
2) Data Scientist, Shelf
https://bit.ly/3lkR0GS
3) Experienced ML Engineer, Lun
https://bit.ly/3ffbfCh
4) Machine learning architect, SoftServe
https://bit.ly/2Vn1hY9
5) Lead MLOps engineer, SoftServe
https://bit.ly/3zMqFWc
6) Product Data Scientist, Snap
https://bit.ly/3if9QgE
7) Senior Machine Learning Engineer, Sigma Software
https://bit.ly/2Vjfh5x
8) Senior Machine Learning Scientist, Upwork
https://bit.ly/3C0ozE4
9) Product Manager, Machine Learning, Grammarly
https://bit.ly/3f9k93V
10) Deep Learning Research Engineer (RnD), Reface
https://bit.ly/3rIspNr
11) Computer Vision Research Engineer, SQUAD
https://bit.ly/3rGkHUe
Sourcing top tech talent is always a challenge... Yeap, we've all been there!
Data Phoenix can offer some help. Did you know that you can publish your job opportunities in our digest for free? If you have open positions in Data Science, Machine Learning, Data Engineering, MLOps, etc., don't hesitate to contact us!🤗
Contact email📥: editor@dataphoenix.info
1) Machine Learning Engineer, Shelf
https://bit.ly/3icOAIq
2) Data Scientist, Shelf
https://bit.ly/3lkR0GS
3) Experienced ML Engineer, Lun
https://bit.ly/3ffbfCh
4) Machine learning architect, SoftServe
https://bit.ly/2Vn1hY9
5) Lead MLOps engineer, SoftServe
https://bit.ly/3zMqFWc
6) Product Data Scientist, Snap
https://bit.ly/3if9QgE
7) Senior Machine Learning Engineer, Sigma Software
https://bit.ly/2Vjfh5x
8) Senior Machine Learning Scientist, Upwork
https://bit.ly/3C0ozE4
9) Product Manager, Machine Learning, Grammarly
https://bit.ly/3f9k93V
10) Deep Learning Research Engineer (RnD), Reface
https://bit.ly/3rIspNr
11) Computer Vision Research Engineer, SQUAD
https://bit.ly/3rGkHUe
Sourcing top tech talent is always a challenge... Yeap, we've all been there!
Data Phoenix can offer some help. Did you know that you can publish your job opportunities in our digest for free? If you have open positions in Data Science, Machine Learning, Data Engineering, MLOps, etc., don't hesitate to contact us!🤗
Contact email📥: editor@dataphoenix.info
📌Customer Support Automation Platform at Uber
In this comprehensive article, you'll learn how Uber manages customer support by using it AI/ML-powered automation platform. Dig in to explore the technological belly of their procedures, architecture, and infrastructure for democratizing the customer support policies
https://ubr.to/3j4Qb2h
In this comprehensive article, you'll learn how Uber manages customer support by using it AI/ML-powered automation platform. Dig in to explore the technological belly of their procedures, architecture, and infrastructure for democratizing the customer support policies
https://ubr.to/3j4Qb2h
Uber Engineering Blog
Customer Support Automation Platform at Uber
High Level Overview of the Problem Introduction If you’ve used any online/digital service, chances are that you are familiar with what a typical customer se
What to read during the weekend?🧐
Handbook of Artificial Intelligence!
The Handbook of Artificial Intelligence, Volume I focuses entirely on the progress that has been happening in the field, as well as on various applications of AI. The book elaborates on AI, AI in the literature, problem representation, search methods, and sample search programs. The text then ponders on representation of knowledge, including survey of representation techniques and representation schemes. The manunoscript explores understanding natural languages, as well as machine translation, grammar, parsing, test generation, and natural language processing systems. The book also takes a look at understanding of spoken language, including systems architecture and the ARPA SUR projects. The text is a valuable source of information for computer science experts and researchers interested in pursuing further research in artificial intelligence.
Handbook of Artificial Intelligence!
The Handbook of Artificial Intelligence, Volume I focuses entirely on the progress that has been happening in the field, as well as on various applications of AI. The book elaborates on AI, AI in the literature, problem representation, search methods, and sample search programs. The text then ponders on representation of knowledge, including survey of representation techniques and representation schemes. The manunoscript explores understanding natural languages, as well as machine translation, grammar, parsing, test generation, and natural language processing systems. The book also takes a look at understanding of spoken language, including systems architecture and the ARPA SUR projects. The text is a valuable source of information for computer science experts and researchers interested in pursuing further research in artificial intelligence.
💡Model Health Assurance at LinkedIn
Learn about Pro-ML, LinkedIn's centralized ML platform that hosts hundreds of AI models running in production, helping ensure a world-class product experience to its customers and members. Health assurance (HA) is a key component of the platform.
https://bit.ly/37ebjxG
Learn about Pro-ML, LinkedIn's centralized ML platform that hosts hundreds of AI models running in production, helping ensure a world-class product experience to its customers and members. Health assurance (HA) is a key component of the platform.
https://bit.ly/37ebjxG
Linkedin
Model health assurance platform at LinkedIn
Co-authors: Rajeev Kumar, Dhritiman Das, Saatwik Nagpal, Shubham Gupta, and Vikram Singh
In-depth Guide to ML Model Debugging and Tools You Need to Know
ML systems are trickier to test than traditional software. In this guide, you'll learn some debugging strategies for ML models and the tools to implement them. Model interpretability will also be discussed, showing how to trace the path of errors from the input to the output.
https://bit.ly/3ykWmWz
ML systems are trickier to test than traditional software. In this guide, you'll learn some debugging strategies for ML models and the tools to implement them. Model interpretability will also be discussed, showing how to trace the path of errors from the input to the output.
https://bit.ly/3ykWmWz
neptune.ai
In-depth Guide to ML Model Debugging and Tools You Need to Know
Everyone is excited about machine learning, but only a few know and understand the limitations that keep ML from widespread adoption. ML models are great at specific tasks, but they can also get a lot of things wrong. The key to a successful project is understanding…
Hi all!👋🏻
We at Data Phoenix strive to create a truly immersive, creative environment that empowers our subscribers and followers to unleash their talent and to demonstrate their expertise to the world.
How exactly, you may wonder?
We offer you all an open platform to publish and promote your tech content: articles, guides, research posts, etc. We are looking for original content, to ensure that every reader is satisfied.
Interested? Just drop us a line at editor@dataphoenix.info, and let's discuss the details!
We at Data Phoenix strive to create a truly immersive, creative environment that empowers our subscribers and followers to unleash their talent and to demonstrate their expertise to the world.
How exactly, you may wonder?
We offer you all an open platform to publish and promote your tech content: articles, guides, research posts, etc. We are looking for original content, to ensure that every reader is satisfied.
Interested? Just drop us a line at editor@dataphoenix.info, and let's discuss the details!
YOLOX: Exceeding YOLO Series in 2021
The paper featuring some improvements that were made to YOLO series, to create a new high-performance detector called YOLOX. It includes the results of testing the detector.
https://bit.ly/3CkEkGm
The paper featuring some improvements that were made to YOLO series, to create a new high-performance detector called YOLOX. It includes the results of testing the detector.
https://bit.ly/3CkEkGm
Sourcing top tech talent is always a challenge... Yeap, we've all been there!
Data Phoenix can offer some help. Did you know that you can publish your job opportunities in our digest for free? If you have open positions in Data Science, Machine Learning, Data Engineering, MLOps, etc., don't hesitate to contact us!
Contact email: editor@dataphoenix.info
Data Phoenix can offer some help. Did you know that you can publish your job opportunities in our digest for free? If you have open positions in Data Science, Machine Learning, Data Engineering, MLOps, etc., don't hesitate to contact us!
Contact email: editor@dataphoenix.info
You Do Not Need a Bigger Boat: Recommendations at Reasonable Scale in a (Mostly) Serverless and Open Stack
The authors argue that immature data pipelines are preventing practitioners from leveraging the latest research on recommender systems. Check out what they propose with Serverless.
https://bit.ly/2Vx9cSP
The authors argue that immature data pipelines are preventing practitioners from leveraging the latest research on recommender systems. Check out what they propose with Serverless.
https://bit.ly/2Vx9cSP
Are you on board and receive our weekly newsletter? Not yet? Well, no need to wait! 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/3wHOPPM
https://bit.ly/3wHOPPM
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.
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
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