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Data Phoenix
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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. Idea and implementation: @dmitryspodarets
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💡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
📚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
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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
💥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
📚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
💥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
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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!
💡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
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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
​​💥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
​​⚡️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
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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
​​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
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Data Phoenix pinned «​​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…»
📚PP-ShiTu: A Practical Lightweight Image Recognition System

PP-ShiTu is a practical lightweight image recognition system that uses metric learning, deep hash, knowledge distillation, and model quantization, to improve accuracy and inference speed.

https://bit.ly/3fb1sMS
💡Visualizing Decision Trees with Pybaobabdt

In this article, you’ll learn how to enable decision tree visualization and model interpretation. It comes with a free package of solutions for all tasks you may need to accomplish.

https://bit.ly/3qj7pOr
📚AI and the Everything in the Whole Wide World Benchmark

In this paper, the authors explore the limits of AI benchmarks to reveal the construct validity issues in their framing as the functionally "general" broad measures of intended progress.

https://bit.ly/3qlqO10
💥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/3ryvhgv