💡Accelerating Inference Up to 6x Faster in PyTorch with Torch-TensorRT
Torch-TensorRT is the new integration of PyTorch with NVIDIA TensorRT, which accelerates the inference with one line of code. Learn how you can start using it today!
https://bit.ly/3eqGY2g
Torch-TensorRT is the new integration of PyTorch with NVIDIA TensorRT, which accelerates the inference with one line of code. Learn how you can start using it today!
https://bit.ly/3eqGY2g
NVIDIA Developer Blog
Accelerating Inference Up to 6x Faster in PyTorch with Torch-TensorRT | NVIDIA Developer Blog
Torch-TensorRT is a PyTorch integration for TensorRT inference optimizations on NVIDIA GPUs. With just one line of code, it speeds up performance up to 6x.
🎄This is the most magical period of the year!
So, even if you are a hardcore science guy, let’s give each other a chance to believe in something special! Data Phoenix team wishes you health, peace, love, and joy this holiday season and throughout 2022. We appreciate every single one of you and we hope that in 2022 we would expand our family of data enthusiasts even more.
❄️Merry Christmas and Happy New Year!
So, even if you are a hardcore science guy, let’s give each other a chance to believe in something special! Data Phoenix team wishes you health, peace, love, and joy this holiday season and throughout 2022. We appreciate every single one of you and we hope that in 2022 we would expand our family of data enthusiasts even more.
❄️Merry Christmas and Happy New Year!
💥Hello everyone! Data Phoenix Speaking!
We are ready to present our weekly issue of the digest! And it is already waiting for you on our website! Tap on the link and feel free to subscribe 👇🏻
https://bit.ly/3qlzVO4
We are ready to present our weekly issue of the digest! And it is already waiting for you on our website! Tap on the link and feel free to subscribe 👇🏻
https://bit.ly/3qlzVO4
Data Phoenix
Data Phoenix Digest - ISSUE 37
Data Phoenix team looking for speakers, unsupervised anomaly detection in Python, GAN training challenges, Player of Games, GAN-Supervised dense visual alignment, NeRF, ICON, SeqFormer, videos, jobs, and more ...
📚Donut: Document Understanding Transformer without OCR
Donut is a novel VDU model that is end-to-end trainable without OCR framework designed to pre-train the model to mitigate the dependencies on large-scale real document images.
https://bit.ly/3FrukMs
Donut is a novel VDU model that is end-to-end trainable without OCR framework designed to pre-train the model to mitigate the dependencies on large-scale real document images.
https://bit.ly/3FrukMs
⚡️Hello everyone & Merry Christmas!
We hope that your weekend is going great!
Data Phoenix prepared for you the list of free vacancies for the week. Kindly check it out and let us know what you think 😉
1) Machine Learning architect - SoftServe (Odesa, Kyiv, Lviv)
https://bit.ly/3poR6iC
2) Computer Vision Engineer - YouScan (Kyiv)
https://bit.ly/30WASDO
3) ML/CV Engineer - Samsung R&D Institute Ukraine (Kyiv)
https://bit.ly/32qURvd
4) Middle+/Senior Data scientist - Autodoc (Odesa, Kyiv, Remote)
https://bit.ly/3FujAgu
5) Senior Data Scientist - Capgemini Engineering (Odesa, Kyiv, Remote)
https://bit.ly/3Fs3NPd
📌Looking to feature your open positions in the digest? Kindly reach out to us at editor@dataphoenix.info for details. We'll be proud to help your business thrive!
We hope that your weekend is going great!
Data Phoenix prepared for you the list of free vacancies for the week. Kindly check it out and let us know what you think 😉
1) Machine Learning architect - SoftServe (Odesa, Kyiv, Lviv)
https://bit.ly/3poR6iC
2) Computer Vision Engineer - YouScan (Kyiv)
https://bit.ly/30WASDO
3) ML/CV Engineer - Samsung R&D Institute Ukraine (Kyiv)
https://bit.ly/32qURvd
4) Middle+/Senior Data scientist - Autodoc (Odesa, Kyiv, Remote)
https://bit.ly/3FujAgu
5) Senior Data Scientist - Capgemini Engineering (Odesa, Kyiv, Remote)
https://bit.ly/3Fs3NPd
📌Looking to feature your open positions in the digest? Kindly reach out to us at editor@dataphoenix.info for details. We'll be proud to help your business thrive!
🔥Hello friends!
We hope your Sunday is going great and you are ready for the upcoming week! But first things first, here's your weekly dose of positivity🤗
https://bit.ly/3Hii81j
We hope your Sunday is going great and you are ready for the upcoming week! But first things first, here's your weekly dose of positivity🤗
https://bit.ly/3Hii81j
📌5 Anomaly Detection Algorithms every Data Scientist should know
There are many reasons for anomalies to occur in your dataset. In this article, you’ll find an overview and a comparison of the best anomaly detection algorithms for outlier detection.
https://bit.ly/3eux7J3
There are many reasons for anomalies to occur in your dataset. In this article, you’ll find an overview and a comparison of the best anomaly detection algorithms for outlier detection.
https://bit.ly/3eux7J3
Medium
5 Anomaly Detection Algorithms every Data Scientist should know
Comparing anomaly detection algorithms for Outlier detection
💫Happy Monday, friends! Let’s start our week in the company of Mike Quindazzi
He’s a managing director leading sales for US Digital Alliances at PwC. He’s been investing his time in gathering industry experience and crafting his management. He’s responsible for nurturing a $1.5 billion cross-sector digital practice by developing innovative approaches and resolving complex issues for clients.
Mike’s greatest reward is helping his clients grow by tapping into their competitive advantages whether that entails global expansion, accelerating digital growth, improving customer experience, transforming organizations, or implementing complex systems for HR/ERP.
He works with diverse & dynamic teams across a range of clients & tech alliances. Whether we think of an acquisition, a minority stake investment, or IPO, Mike focuses on creating value in each and every transaction. He scopes value through effective integration, unlocking synergies, and creating new structures.
https://bit.ly/3ExyaT9
He’s a managing director leading sales for US Digital Alliances at PwC. He’s been investing his time in gathering industry experience and crafting his management. He’s responsible for nurturing a $1.5 billion cross-sector digital practice by developing innovative approaches and resolving complex issues for clients.
Mike’s greatest reward is helping his clients grow by tapping into their competitive advantages whether that entails global expansion, accelerating digital growth, improving customer experience, transforming organizations, or implementing complex systems for HR/ERP.
He works with diverse & dynamic teams across a range of clients & tech alliances. Whether we think of an acquisition, a minority stake investment, or IPO, Mike focuses on creating value in each and every transaction. He scopes value through effective integration, unlocking synergies, and creating new structures.
https://bit.ly/3ExyaT9
📚Player of Games
Player of Games is the first algorithm to achieve strong empirical performance in large perfect and imperfect information games. It reaches strong performance in various games, from Go to poker.
https://bit.ly/3H87n1o
Player of Games is the first algorithm to achieve strong empirical performance in large perfect and imperfect information games. It reaches strong performance in various games, from Go to poker.
https://bit.ly/3H87n1o
💡Speech Recognition in Real-Time using Python
In this post, you’ll find a step-by-step guide explaining how to convert your speech to text in real-time using Python. Let’s learn more about live transcription together!
https://bit.ly/345ngrc
In this post, you’ll find a step-by-step guide explaining how to convert your speech to text in real-time using Python. Let’s learn more about live transcription together!
https://bit.ly/345ngrc
Medium
Speech Recognition in Real-Time using Python
Step-by-step Guide to Live Transcription
📚SeqFormer: a Frustratingly Simple Model for Video Instance Segmentation
SeqFormer is a simple model for video instance segmentation designed to follow the principle of vision transformer that models instance relationships among video frames.
https://bit.ly/3mF5ilS
SeqFormer is a simple model for video instance segmentation designed to follow the principle of vision transformer that models instance relationships among video frames.
https://bit.ly/3mF5ilS
🔥Hello friends!
We hope that your week is going well so far. Data Phoenix team wants to remind you about our weekly newsletter which is coming, as always, tomorrow! 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/3FJCn7v
We hope that your week is going well so far. Data Phoenix team wants to remind you about our weekly newsletter which is coming, as always, tomorrow! 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/3FJCn7v
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.
👍1
🎥 NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis
View Synthesis is a tricky problem. Learn how NeRF embeds an entire scene into the weights of a feedforward neural network to achieve state-of-the-art view synthesis.
https://bit.ly/3Jxy0Pm
View Synthesis is a tricky problem. Learn how NeRF embeds an entire scene into the weights of a feedforward neural network to achieve state-of-the-art view synthesis.
https://bit.ly/3Jxy0Pm
YouTube
NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis (ML Research Paper Explained)
#nerf #neuralrendering #deeplearning
View Synthesis is a tricky problem, especially when only given a sparse set of images as an input. NeRF embeds an entire scene into the weights of a feedforward neural network, trained by backpropagation through a differential…
View Synthesis is a tricky problem, especially when only given a sparse set of images as an input. NeRF embeds an entire scene into the weights of a feedforward neural network, trained by backpropagation through a differential…
Hello friends!
Data Phoenix team congratulates everyone with the coming New 2022 year!🎄
We are ready to present our weekly issue of the digest - the last one this year! And it is already waiting for you on our website! Tap on the link and feel free to subscribe 👇🏻
https://bit.ly/3FNjBfy
Data Phoenix team congratulates everyone with the coming New 2022 year!🎄
We are ready to present our weekly issue of the digest - the last one this year! And it is already waiting for you on our website! Tap on the link and feel free to subscribe 👇🏻
https://bit.ly/3FNjBfy
Data Phoenix
Data Phoenix Digest - ISSUE 38
Distillation of BERT-like models, getting started with Comet ML, hands-on with SciKit-Learn feature-engineering, ensembling off-the-shelf models for GAN training, generative art using neural visual grammars and dual encoders, DSP-SLAM, jobs, and more ...
📌Unsupervised Anomaly Detection in Python
Why’s it so important for data scientists to master anomaly detection? Check out this beginner’s guide to learn all the ins and outs of unsupervised anomaly detection.
https://bit.ly/3sOvQ7Z
Why’s it so important for data scientists to master anomaly detection? Check out this beginner’s guide to learn all the ins and outs of unsupervised anomaly detection.
https://bit.ly/3sOvQ7Z
Medium
Unsupervised Anomaly Detection in Python
A beginner’s guide
⚡️Hello everyone!
We hope that your year started great!
Data Phoenix prepared for you the list of free vacancies for the week. Kindly check it out and let us know what you think 😉
1) Major Donor Data Specialist - Wikimedia Foundation (Remote)
https://bit.ly/32OYn2g
2) Sr. Data Engineer - HashiCorp (US - Remote)
https://bit.ly/3mMKAk5
3) Machine Learning Architect - SoftServe (Odesa, Kyiv, Lviv...)
https://bit.ly/3EIQA3q
4) Senior/Middle CV/ML Engineer - Apostera (Odesa, Kyiv, Remote)
https://bit.ly/3eIrqr8
5) Data Scientist - Snap (Odesa, Kyiv)
https://bit.ly/3eIjSEw
Looking to feature your open positions in the digest? Kindly reach out to us at editor@dataphoenix.info for details. We'll be proud to help your business thrive!
We hope that your year started great!
Data Phoenix prepared for you the list of free vacancies for the week. Kindly check it out and let us know what you think 😉
1) Major Donor Data Specialist - Wikimedia Foundation (Remote)
https://bit.ly/32OYn2g
2) Sr. Data Engineer - HashiCorp (US - Remote)
https://bit.ly/3mMKAk5
3) Machine Learning Architect - SoftServe (Odesa, Kyiv, Lviv...)
https://bit.ly/3EIQA3q
4) Senior/Middle CV/ML Engineer - Apostera (Odesa, Kyiv, Remote)
https://bit.ly/3eIrqr8
5) Data Scientist - Snap (Odesa, Kyiv)
https://bit.ly/3eIjSEw
Looking to feature your open positions in the digest? Kindly reach out to us at editor@dataphoenix.info for details. We'll be proud to help your business thrive!
🔥Hello everybody!
We hope your Sunday is going great and you are ready for the upcoming week! But first things first, here's your weekly dose of positivity🤗
https://bit.ly/3mPEEXh
We hope your Sunday is going great and you are ready for the upcoming week! But first things first, here's your weekly dose of positivity🤗
https://bit.ly/3mPEEXh
💡Paper Review: NL-Augmenter A Framework for Task-Sensitive Natural Language Augmentation
This paper review shares a look at a new participatory Python-based natural language augmentation framework that supports the creation of transformations and filters.
https://bit.ly/3HtXXgN
This paper review shares a look at a new participatory Python-based natural language augmentation framework that supports the creation of transformations and filters.
https://bit.ly/3HtXXgN
Andlukyane
Paper Review: NL-Augmenter A Framework for Task-Sensitive Natural Language Augmentation – Andrey Lukyanenko
My review of the paper NL-Augmenter A Framework for Task-Sensitive Natural Language Augmentation and my contribution to it
📚Ensembling Off-the-shelf Models for GAN Training
Nupur Kumari et al. propose an effective selection mechanism for pretrained CV models. It allows to choose the most accurate model, and progressively add it to the discriminator ensemble.
https://bit.ly/3JBmuSR
Nupur Kumari et al. propose an effective selection mechanism for pretrained CV models. It allows to choose the most accurate model, and progressively add it to the discriminator ensemble.
https://bit.ly/3JBmuSR
💥Hello friends! Let's take a look at Yann LeCun. He is best known for his work in deep learning and the invention of the convolutional network method which is widely used for image, video, and speech recognition.
He is VP and Chief AI Scientist at Facebook and Silver Professor at NYU affiliated with the Courant Institute and the Center for Data Science. Yann was the founding Director of Facebook AI Research and of the NYU Center for Data Science.
In late 2013, LeCun became Director of AI Research at Facebook, while remaining on the NYU Faculty part-time. His research interests include machine learning and artificial intelligence, with applications to computer vision, natural language understanding, robotics, and computational neuroscience. He is the recipient of the 2018 ACM Turing Award for "conceptual and engineering breakthroughs that have made deep neural networks a critical component of computing".
https://bit.ly/3sUgEGq
He is VP and Chief AI Scientist at Facebook and Silver Professor at NYU affiliated with the Courant Institute and the Center for Data Science. Yann was the founding Director of Facebook AI Research and of the NYU Center for Data Science.
In late 2013, LeCun became Director of AI Research at Facebook, while remaining on the NYU Faculty part-time. His research interests include machine learning and artificial intelligence, with applications to computer vision, natural language understanding, robotics, and computational neuroscience. He is the recipient of the 2018 ACM Turing Award for "conceptual and engineering breakthroughs that have made deep neural networks a critical component of computing".
https://bit.ly/3sUgEGq
💡Distillation of BERT-Like Models: The Theory
Let’s explore the mechanisms behind the approach of DistilBERT, including 101, architectures, distillation loss, and other useful details you may need in your implementation.
https://bit.ly/3JyAcpP
Let’s explore the mechanisms behind the approach of DistilBERT, including 101, architectures, distillation loss, and other useful details you may need in your implementation.
https://bit.ly/3JyAcpP
Medium
Distillation of BERT-Like Models: The Theory
Let’s take a look at how we can apply DistilBERT’s reasoning to distil any of our own BERT-like models.