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Data Phoenix Digest - ISSUE 41

Data Phoenix Digest - ISSUE 41

Overview of the TOP algorithms for ML, distributed training, capacity recommendation engine, time series anomaly detection with PyFBAD, linear algebra with transformers, a ConvNet for the 2020s, JoJoGAN, Plenoxels, videos, courses, jobs, and more ...

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by Dmitry Spodarets

At Data Phoenix, we do our best to share the latest insights with you, and it’s time we added a new exciting channel to communicate — Slack. Please join our new Slack chat to get all the benefits of Data Phoenix while networking with like-minded professionals. Don’t hesitate to get on board — and let's have some fun!



Overview of the TOP Algorithms for Machine Learning. Part 1
In Part 1 of the article, you’ll learn about types of Machine Learning and such algorithms as Linear Regression, K-Nearest Neighbors (kNN), Convolutional Neural Network (CNN).

Advancing Genomics to Better Understand and Treat Disease
Here are recent research and industry developments that the team at Google Health made to help quickly identify genetic disease and foster the equity of genomic tests across ancestries.

Distributed Training: Guide for Data Scientists
In this article, you’ll learn what distributed training is and how it can solve the fundamental problem of training a complex machine learning model on a huge dataset.

How to Use TomTom Data for Predictive Modeling
Predictive modeling is not easy. In this article, we’ll find out how use accident data collected from TomTom API, to learn when and where accidents are most likely to take place.

Capacity Recommendation Engine: Throughput and Utilization Based Predictive Scaling
Capacity Recommendation Engine is a system that relies on throughput and utilization scaling with ML modeling, to show the relationship between the golden signal metrics and service capacity.

Computer Vision-based Anomaly Detection Using Amazon Lookout for Vision and AWS Panorama
In this post, you’ll learn how Tyson Foods Inc., is using CV applications at the edge to automate industrial processes inside their meat processing plants. Learn more in Part 1.

Time Series Anomaly Detection with PyFBAD
The pyfbad library is an end-to-end unsupervised anomaly detection package that provides source codes for all ml-flow steps. Learn how it can be used for time series anomaly detection.


Linear Algebra with Transformers
In this paper, François Charton demonstrates that transformers can be trained to perform numerical calculations with high accuracy instead of just being used for symbolic computation.

Plenoxels: Radiance Fields without Neural Networks
In this paper, Alex Yu et al. introduce Plenoxels (plenoptic voxels), a system for photorealistic view synthesis capable of representing a scene as a sparse 3D grid with spherical harmonics.

JoJoGAN: One Shot Face Stylization
JoJoGAN is a one-shot image stylization project that uses GAN inversion and a pretrained StyleGAN to capture stylistic details that are obvious to humans in the right and efficient manner.

A Static Analyzer for Detecting Tensor Shape Errors in Deep Neural Network Training Code
PyTea is an automatic static analyzer that detects tensor-shape errors in PyTorch code. PyTea's scalability and precision hinges on the characteristics of real-world PyTorch applications.

A ConvNet for the 2020s
In this paper, Zhuang Liu et al. present a family of pure ConvNet models dubbed ConvNeXt. ConvNeXts compete favorably with Transformers in terms of accuracy and scalability.


Top 10 AI and ML developer updates from Google I/O 2021
Check out this short, 5-minute video to explore the top 10 AI and ML developer updates from this year’s Google I/O, as presented by Google’s AI Lead Laurence Moroney.


Deep Learning Course by New York University
This 14-week course covers in detail 8+ deep learning topics (from historical context to machine translation tasks) and includes extensive lectures, labs, and notebooks.


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by Dmitry Spodarets

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