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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โšก๏ธHello everyone!
Data Phoenix team is 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 ๐Ÿ‘‡๐Ÿป
https://bit.ly/3dBnO9R
โ€‹โ€‹โšก๏ธ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 ๐Ÿ˜‰

1) Senior CV Engineer at SoftServe (Odesa, Lviv, Kyiv, Remote)
https://bit.ly/3GqJdii
2) Summer Internship, Data Scientist at Spotify (London)
https://bit.ly/3pOC9p2
3) Machine Learning Engineer at Lyft (Kyiv)
https://bit.ly/31QBACS
4) Sr. Data Scientist at GoPro (San Mateo, Carlsbad)
https://bit.ly/31LhASc
5) Data Scientist at Snap (Odesa, Kyiv, Remote)
https://bit.ly/3lUQgb2

For other available positions click on the link ๐Ÿ‘‰๐Ÿป
https://bit.ly/3yeuW5v
๐Ÿ“šHow to handle ML model drift in production

In this Q&A, you'll learn what to do if you have a model in production, and the data is drifting. Eight awesome and detailed tips to help you solve the problem in the bud.

https://bit.ly/3DJRX1i
โ€‹โ€‹๐Ÿ”ฅHello 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 positivity๐Ÿค—
https://bit.ly/3pNsMpA
๐Ÿ“ŒHyperStyle: StyleGAN Inversion with HyperNetworks for Real Image Editing

In this paper, Yuval Alaluf et al. propose HyperStyle, a hypernetwork that learns to modulate StyleGAN's weights to faithfully express a given image in editable regions of the latent space.

https://bit.ly/3oLRhUE
โ€‹โ€‹โšก๏ธHello, friends! Letโ€™s start this week in a company of Tom Davenport, a world-renowned thought leader in all things IT.

Dr. Davenport is the Presidentโ€™s Distinguished Professor of Information Technology and Management at Babson College, a Fellow of the MIT Center for Digital Business, and an independent senior advisor to Deloitte Analytics.

He is also the author and co-author of 20 books and more than 200 articles. He is dedicated to helping organizations transform their management practices in digital business domains, such as artificial intelligence, analytics, and enterprise systems.

Over the course of 40 years, he has written or edited twenty books and over 250 print or digital articles for Harvard Business Review (HBR), Sloan Management Review, the Financial Times, and many other publications. He earned his Ph.D from Harvard University and has taught at the Harvard Business School, the University of Chicago, and the University of Texas at Austin.

https://bit.ly/30k3DKm
๐Ÿ“šEditGAN: High-Precision Semantic Image Editing

In this paper, Huan Ling et al. present EditGAN, the first GAN-driven image editing framework that outperforms previous editing methods on standard editing benchmark tasks.

https://bit.ly/3s4OI2g
๐Ÿ’กText-based Causal Inference

In this tutorial, you'll learn about a new method of analyzing voter fraud disinformation by estimating causal effect with text as treatment and confounder.

https://bit.ly/3EZm6ee
โšก๏ธHello everyone, it's Data Phoenix speaking!
We hope that your week is going well so far. Our 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/3IXSXm3
๐Ÿ“ŒGet Started: DCGAN for Fashion-MNIST

This tutorial will guide you through the implementation of a Deep Convolutional GAN (DCGAN) with TensorFlow 2 / Keras. It's based on a classic GAN paper.

https://bit.ly/33rQQXo
๐Ÿ’ฅHello 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 ๐Ÿ‘‡๐Ÿป
https://bit.ly/3p3TPOc
โ€‹โ€‹โšก๏ธ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 ๐Ÿ˜‰

1) Senior Data Scientist - Finance Data - Coinbase (Remote - USA)
https://bit.ly/3F9CZTK
2) Senior Software Engineer - Data Pipeline - GitHub (Remote - USA)
https://bit.ly/3mhpDNL
3) Machine Learning Engineer - Cloudflare (Remote - USA)
https://bit.ly/3shnqpd
4) Principal Data Scientist - Intercom (Remote in Ireland or the UK)
https://bit.ly/32k5dg8
5) Cloud Data Engineer - Rackspace (Remote - USA)
https://bit.ly/3E9zCdY

๐Ÿ“ŒLooking 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!
๐Ÿ“ŒLearning Rates for Deep Learning Models

How can you make your deep learning models as effective as possible? In this article, you'll learn about the effects of a learning rate on the convergence and performance of DL models.

https://bit.ly/3e3Q1WV
โ€‹โ€‹๐Ÿ”ฅHello 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 positivity๐Ÿค—
https://bit.ly/3yMTe6L
๐Ÿ“šNรœWA: Visual Synthesis Pre-training for Neural visUal World creAtion

NรœWA is a unified multimodal pre-trained model that can generate new or manipulate existing visual data (i.e., images and videos) for various visual synthesis tasks.

https://bit.ly/33Id9s9
โ€‹โ€‹โšก๏ธHappy Monday, friends! Data Phoenix team wishes you an amazing week! Today, we want to present to you Tamara McCleary

Tamara is the founder and CEO of a global social media marketing agency, Thulium. Currently, sheโ€™s a full-time graduate student at Harvard. As a research scientist at the Harvard Kennedy School, she studies science, technology, ethics, global economics, and public policy.

Tamara is interested in algorithmic governance, as well as the social, moral, ethical, and spiritual implications of genetic engineering. She prefers exploring various existential questions, and the future of religion in the world where life extension technologies, synthetic biology, artificial general intelligence, and our reliance on machines create a transhumanist and post-humanist existence.

She is also an advisor and crew member for the Proudly Human Off-World Projects, which simulates resource-constrained environments in search of solutions for those living off-world.

https://bit.ly/3skl5db
๐Ÿ“šGradInit: Learning to Initialize Neural Networks for Stable and Efficient Training

GradInit is an automated and architecture agnostic method for initializing neural networks. It improves the stability of the original Transformer architecture for machine translation.

https://bit.ly/3pbIRX0
๐Ÿ“ŒMeta-Learning for Keyphrase Extraction

This article explores how to build a key phrase extractor that performs on in-domain data and in zero-shot scenarios where keyphrases need to be extracted from data with different distributions.

https://bit.ly/32sN3J6