Join Dr. Dean and Dr. Bazhirov on November 19th to learn more about Interpretable Machine Learning for materials research and development.
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The exponential growth of interest in Machine Learning has led to a wealth of data in recent years, which has corresponded with an expansion in the number of techniques available. In this webinar, we benchmark a few recent techniques, including SISSO (Symbolic Regression), TPOT (AutoML), Roost (deep learning algorithms), and XGBoost (Gradient-boosting) to predict the properties of perovskites and 2dmaterials.

Register at https://bit.ly/3nuAp4h

💡Serving ML Models in Production: Common Patterns

This article explores Ray Serve, a service combining pipelines, ensemble, business logic, and online learning for machine learning. Learn how to use the service for serving ML models in production.

https://bit.ly/3criUeG

⚡️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 👇🏻
https://bit.ly/3x40Q47

​​⚡️Hello friends! 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) JS Engineer (Meteor+React) at Exabyte.io - please write to d.spodarets@dataphoenix.info directly
2) Software Engineer, Machine Learning at Grammarly (San Francisco; Remote).
https://bit.ly/3oOqRQW
3) Machine Learning Engineer at Amazon (Santa Clara, California, USA).
https://bit.ly/3Fzcln6
4) Machine Learning Engineer at Twilio (Madrid, Spain).
https://bit.ly/3CMAmFz
5) Machine Learning Scientist, Core AI at Amazon (Berlin, Germany).
https://bit.ly/3CA0svf

Looking to feature your open positions in the digest? Kindly reach out to us at editor@dataphoenix.info

📌Solving Math Word Problems

Learn about a system trained by the OpenAI team that solves grade school math problems with twice the accuracy of a fine-tuned GPT-3 model. It solves ~90% as many problems as real kids.

https://bit.ly/30KA5oX

​​🔥Data Phoenix wishes you lovely Sunday! We hope 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/3HQC4cG

📚EditGAN: High-Precision Semantic Image Editing

EditGAN is a novel method for high quality, high precision semantic image editing, allowing users to edit images by modifying their highly detailed part segmentation masks.

https://bit.ly/3cvlAIu

⚡️Hello everyone! How are you feeling about starting a new week? Today, let us introduce you Bernard Marr — a world-renowned futurist, influencer, and thought leader in business and technology. Passionate about using technology for the good of humanity, he shares his vision with over 2 million social media followers and 1 million newsletter subscribers. He was ranked by LinkedIn as one of the top 5 business influencers in the world and the No.1 influencer in the UK.

Today, Bernard Marr is one of the world’s most highly respected experts when it comes to future trends, strategy, business performance, digital transformation, and the intelligent use of data and AI in business. He has worked with and advised many of the world’s best-known organizations, such as Amazon, Google, Microsoft, Astra Zeneca, The Bank of England, BP, NVIDIA, Cisco, DHL, IBM, HPE, Ericsson, Jaguar Land Rover, Mars, The Ministry of Defense, NATO, The Home Office, NHS, Oracle, T-Mobile, Toyota, The Royal Air Force, Shell, The United Nations, Walgreens Alliance Boots, and Walmart.

He is the author of 20 books and hundreds of high profile reports and articles, including the international best-sellers. His books have been translated into over 20 languages and have been repeatedly featured as the Amazon No.1 bestselling book. He has earned the CMI Management Book of the Year award, the Axiom book award, and the WHSmith best business book award.

Today, Bernard also enjoys teaching for Oxford University, Warwick Business School, the Irish Management Institute, and ICAEW. On top of that, Bernard serves as a non-executive director on the board of businesses and has a seat on the dean’s council for Lancaster University Management School.

https://bit.ly/3qXZAOF

💡Hugging Face Transformer Inference Under 1 Millisecond Latency

Hugging Face has released “Infinity’’, a server product that performs inference at enterprise scale. It can perform Transformer inference at 1 millisecond latency on the GPU.

https://bit.ly/3xb5Qnj

📚 On the Frequency Bias of Generative Models

In this paper, the authors provide insights on measures against high-frequency artifacts and what makes them effective, with focus on a frequency bias.

https://bit.ly/3l1m51k

📌Design patterns for Machine Learning Pipelines

ML pipeline design keeps evolving. In this article, you'll learn how these design patterns changed, what processes they went through, and their future direction.

https://bit.ly/32nIRKp

📚DScribe: Library of Descriptors for Machine Learning in Materials Science

DScribe is a software package for ML that provides "descriptors" for atomistic materials simulations, to accelerate and simplify the application of ML for atomistic property prediction.

https://bit.ly/3cKVMIe

🔥Hello friends! We hope that your week is going well this 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/3oYT6g7

💡ORDAINED: The Python Project Template

Creating Python packages can be annoying. Learn about a project boilerplate template for Python packages that can be used instead of copying a directory tree and doing find and replace.

https://bit.ly/3xsJGxg'

📌How to Handle ML Model Drift in Production

Data drift is an everyday challenge in Data Science and Machine Learning. In this introductory overview, you'll learn about major steps you can take to handle it more efficiently.

https://bit.ly/3E12yFT

⚡️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 👇🏻
https://bit.ly/3CWZlpB

​​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/3nZuC6V

​​⚡️Hello friends!
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) Senior/Middle CV/ML Engineer at Apostera (Odesa, Kyiv, Remote)
https://bit.ly/3d0WOQs
2) Junior CV/ML Engineer at Apostera (Odesa, Kyiv, Remote)
https://bit.ly/3d0WOQs
3) Senior Data Scientist/ML Engineer at Xenoss (Odesa, Kyiv, Remote)
https://bit.ly/3I0pszG
4) Middle+/Senior Data scientist at AUTODOC (Odesa)
https://bit.ly/3IaIZxw
5) Machine Learning Architect at SoftServe (Odesa, Kyiv, Remote)
https://bit.ly/3lcoPcE

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! Let’s start our Monday with Francesca Lazzeri, an experienced data scientist, economist, and machine learning practitioner with over 15 years of experience in academic research.

Francesca is the author of “Machine Learning for Time Series Forecasting with Python”. She has published numerous articles and papers in technology journals.

In addition to that, she is a Professor of Machine Learning at Columbia University and a Principal Data Scientist Manager at Microsoft, where she leads a team of data scientists focusing on the data science and machine learning applications in such use cases as customer retention, fraud detection, and experimentation.

She was a research fellow at Harvard University in the Technology and Operations Management Unit. Also, she is an Advisory Board Member for the European Union and for the WiDS initiative, a machine learning mentor at the Massachusetts Institute of Technology.

Let us know, who should be next?

https://bit.ly/3rjcUNJ

💡Deceive D: Adaptive Pseudo Augmentation for GAN Training with Limited Data

In this paper, Liming Jiang et al. introduce a novel strategy called Adaptive Pseudo Augmentation (APA) that encourages healthy competition between the generator and the discriminator.

https://bit.ly/317tbdL

📌Get Started: DCGAN for Fashion-MNIST

In this tutorial for beginners, you'll implement a typical DCGAN with TensorFlow 2 and Keras, based on a basic GAN paper and a Colab notebook.

https://bit.ly/3xGGxK3