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.
📚Generative Art Using Neural Visual Grammars and Dual Encoders
In this paper, Chrisantha Fernando et al. present a novel algorithm for producing generative art. It allows a user to input a text string that outputs an image that interprets that string.
https://bit.ly/3sUUNPb
In this paper, Chrisantha Fernando et al. present a novel algorithm for producing generative art. It allows a user to input a text string that outputs an image that interprets that string.
https://bit.ly/3sUUNPb
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📌Mixed Neural Style Transfer With Two Style Images
Neural style transfer (NST) is a fascinating field. Let’s learn how to apply the styles of two images to one photo, analyze the improvement process and show how to extend NST optimization.
https://bit.ly/3n3M9Kn
Neural style transfer (NST) is a fascinating field. Let’s learn how to apply the styles of two images to one photo, analyze the improvement process and show how to extend NST optimization.
https://bit.ly/3n3M9Kn
Medium
Mixed Neural Style Transfer With Two Style Images
An approach to extend neural style transfer to include multiple styles
💥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/3pYWhGc
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/3pYWhGc
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.
📚Implicit MLE: Backpropagating Through Discrete Exponential Family Distributions
Implicit Maximum Likelihood Estimation (I-MLE) is a framework for end-to-end learning of models combining discrete exponential family distributions and differentiable neural components.
https://bit.ly/32ZEDJD
Implicit Maximum Likelihood Estimation (I-MLE) is a framework for end-to-end learning of models combining discrete exponential family distributions and differentiable neural components.
https://bit.ly/32ZEDJD
💥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/3F3prs8
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/3F3prs8
Data Phoenix
Data Phoenix Digest - ISSUE 39
Data Phoenix team looking for speakers, introduction to clustering in Python with PyCaret, OCR passports with OpenCV and Tesseract, visualizing decision trees with Pybaobabdt, Pix2Pix, PP-ShiTu, FuseDream, PartImageNet, jobs, and more ...
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Data Phoenix pinned «💥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/3F3prs8»
⚡️Hello everyone!
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) AI / Data Science Engineer - Reply (Kyiv, Dnipro, Remote)
https://bit.ly/3HPIVCl
2) Senior/Middle CV/ML Engineer - Apostera (Odesa, Kyiv, Remote)
https://bit.ly/3zCuFtG
3) Data Scientist - ROZETKA (Remote)
https://bit.ly/3F4RaZd
4) Machine Learning Architect - SoftServe (Odesa, Kyiv, Lviv)
https://bit.ly/3qT37fE
5) Data Scientist - Snap (Odesa, Kyiv)
https://bit.ly/3q6KcyX
📌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) AI / Data Science Engineer - Reply (Kyiv, Dnipro, Remote)
https://bit.ly/3HPIVCl
2) Senior/Middle CV/ML Engineer - Apostera (Odesa, Kyiv, Remote)
https://bit.ly/3zCuFtG
3) Data Scientist - ROZETKA (Remote)
https://bit.ly/3F4RaZd
4) Machine Learning Architect - SoftServe (Odesa, Kyiv, Lviv)
https://bit.ly/3qT37fE
5) Data Scientist - Snap (Odesa, Kyiv)
https://bit.ly/3q6KcyX
📌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!
💡Hyperparameter Tuning of Neural Networks with Optuna and PyTorch
In this article, you’ll learn how to tune hyperparameters in neural networks in PyTorch and how to find that perfect neural networks model with the help of Optuna.
https://bit.ly/3t7imnX
In this article, you’ll learn how to tune hyperparameters in neural networks in PyTorch and how to find that perfect neural networks model with the help of Optuna.
https://bit.ly/3t7imnX
Towards Data Science
Hyperparameter Tuning of Neural Networks with Optuna and PyTorch | Towards Data Science
How to find that perfect neural networks model for our use case with the help of Optuna
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🔥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/3f4eJa3
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/3f4eJa3
💥Data Phoenix team will renew our "The A-Z of Data" webinars at the end of January
We're looking for speakers to collaborate on these activities. If you are looking for a platform with a relevant audience and you have experience and knowledge to share, we'll be glad to see you among the speakers. If you are our candidate or know someone who might be interested, let us know by email at editor@dataphoenix.info
https://bit.ly/3HQQ05k
We're looking for speakers to collaborate on these activities. If you are looking for a platform with a relevant audience and you have experience and knowledge to share, we'll be glad to see you among the speakers. If you are our candidate or know someone who might be interested, let us know by email at editor@dataphoenix.info
https://bit.ly/3HQQ05k
⚡️Hello everyone! How are you feeling about starting a new week? Today, let us introduce you, Kai-Fu Lee. He is the Chairman and CEO of Sinovation Ventures, a leading technology venture capital focusing on developing the next generation of Chinese high-tech companies.
Prior to founding Sinovation in 2009, Dr. Lee was the President of Google China. Previously, he held executive positions at Microsoft, SGI, and Apple.
Dr. Lee founded Microsoft Research China, which was named the hottest research lab by MIT Technology Review. Later renamed Microsoft Research Asia, this institute trained the great majority of AI leaders in China, including CTOs or AI heads at Baidu, Tencent, Alibaba, Lenovo, Huawei, and Haier.
While with Apple, Dr. Lee led AI projects in speech and natural language, which have been featured on Good Morning America on ABC Television and the front page of Wall Street Journal.
https://bit.ly/3K7F5GL
Prior to founding Sinovation in 2009, Dr. Lee was the President of Google China. Previously, he held executive positions at Microsoft, SGI, and Apple.
Dr. Lee founded Microsoft Research China, which was named the hottest research lab by MIT Technology Review. Later renamed Microsoft Research Asia, this institute trained the great majority of AI leaders in China, including CTOs or AI heads at Baidu, Tencent, Alibaba, Lenovo, Huawei, and Haier.
While with Apple, Dr. Lee led AI projects in speech and natural language, which have been featured on Good Morning America on ABC Television and the front page of Wall Street Journal.
https://bit.ly/3K7F5GL
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📌Introduction to Clustering in Python with PyCaret
PyCaret is an open-source, low-code ML library in Python that automates ML workflows. Let’s learn how you can enable and do unsupervised clustering tasks in Python with it.
https://bit.ly/3HW0OiZ
PyCaret is an open-source, low-code ML library in Python that automates ML workflows. Let’s learn how you can enable and do unsupervised clustering tasks in Python with it.
https://bit.ly/3HW0OiZ
Medium
Introduction to Clustering in Python with PyCaret
A step-by-step, beginner-friendly tutorial for unsupervised clustering tasks in Python using PyCaret
Hello friends👋🏻
Data Phoenix speaking! Our team has great news to share! We want to be close to you as much as possible, that’s why we created Slack chat where we can talk to you 24/7, you can text what you expect to see on our social media, what would you like to have more or less, also you can find friends who are sharing the same interests as you. Isn’t it amazing? Tap on the link and let's have some fun!
https://bit.ly/3ngiU7b
Data Phoenix speaking! Our team has great news to share! We want to be close to you as much as possible, that’s why we created Slack chat where we can talk to you 24/7, you can text what you expect to see on our social media, what would you like to have more or less, also you can find friends who are sharing the same interests as you. Isn’t it amazing? Tap on the link and let's have some fun!
https://bit.ly/3ngiU7b
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