📌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
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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
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
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
huggingface.co
Introduction - Hugging Face NLP Course
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
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…»
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/3Cd0sSZ
https://bit.ly/36UjEGI
https://bit.ly/3Cd0sSZ
https://bit.ly/36UjEGI
Data Phoenix
Data Phoenix Digest - 05.08.2021
Webinar "The A-Z of Data: Introduction to MLOps", chip design with ML, KNN algorithm, model health assurance at LinkedIn, YOLOX, CoBERL, SynLiDAR, PaddleSeg, Real-ESRGAN, courses, competitions, jobs, and more...
💡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
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.
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
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
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
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
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
YouTube
CS W182 / 282A at UC Berkeley Designing, Visualizing and Understanding Deep Neural Networks - Deep Learnings - 2021 - YouTube