Towards Data Science: The Basics: Time Series and Seasonal Decomposition
http://bit.ly/2Qu3pIr

When are time series techniques appropriate and how do you perform decomposition?Continue reading on Towards Data Science »

Towards Data Science: Revisiting a Data Science Totem ~ Variables
http://bit.ly/393A9Pk

Elevate your data science projects by following these variables’ mapContinue reading on Towards Data Science »

Towards Data Science: 7 Habits of Highly Effective Programmers
http://bit.ly/2UmgSDg

About 40 to 45 percent of what we programmers do every day is something we do on autopilotContinue reading on Towards Data Science »

Monitoring Machine Learning Models in Production

Once you deploy your model to production, the work isn't over. This guide shows how to monitor your models, why that matters, and how to go about implementing your own ML monitoring solutions.

https://bit.ly/35lfaaj

YOLO v4 released! - Improve speed and better object detection accurately

Compared with the previous YOLOv3, YOLOv4 has the following advantages:
- It is an efficient and powerful object detection model that enables anyone with a 1080 Ti or 2080 Ti GPU to train a super fast and accurate object detector.
- The influence of state-of-the-art «Bag-of-Freebies» and «Bag-of-Specials» object detection methods during detector training has been verified.
- The modified state-of-the-art methods, including CBN (Cross-iteration batch normalization), PAN (Path aggregation network), etc., are now more efficient and suitable for single GPU training.

Paper: https://bit.ly/2YzFj3R
GitHub: https://bit.ly/2WrECHh

On Guard Against COVID-19: AI Projects That Deserve a Shout-Out

In this article, the author analyzed some of the most interesting and promising solutions in terms of their potential to slow down the spread of the COVID-19 infection, reduce death rates, and improve information hygiene in these turbulent times.

https://bit.ly/2A82qZn

Optimize Response Time of your Machine Learning API In Production

This article demonstrates how building a smarter API serving Deep Learning models minimizes the response time.

https://bit.ly/2L2LIg9

ICLR 2020 Recordings

All recordings for papers and workshops of the International Conference on Learning Representations 2020 are now available to everyone!

Workshops - https://bit.ly/3dm5S0C
Papers - https://bit.ly/3dgubgu

Feature Stores for ML

This is a great collection of talks about the variety of ways that organizations build and manage their feature stores.

https://bit.ly/2LGV7dB

Model Evaluation Metrics in Machine Learning

A detailed explanation of model evaluation metrics to evaluate a classification machine learning model.

https://bit.ly/3gvS7Pf

FP64, FP32, FP16, BFLOAT16, TF32, and other members of the ZOO

There are many floating point formats you can hear about in the context of deep learning. Here is a summary of what are they about and where are they used.

https://bit.ly/3gUS2Vv

Classification of Brain MRI as Tumor/Non Tumor

Learn to train and apply a simple CNN to differentiate between an MRI with a Tumor and an MRI without one.

https://bit.ly/3gX9clr

Beyond fashion: Deep Learning with Catalyst

Step-by-step tutorial for setting up a deep learning pipeline with Catalyst and deploying the model to production.

https://bit.ly/2XATNQa

How to Scale Data With Outliers for Machine Learning

In this tutorial, you will discover how to use robust scaler transforms to standardize numerical input variables for classification and regression.

https://bit.ly/3eZginS

How to Use Polynomial Feature Transforms for Machine Learning

In this tutorial, you will discover how to use polynomial feature transforms for feature engineering with numerical input variables.

https://bit.ly/3h3dUy8

Catalyst 101 — Accelerated PyTorch

In this post, you will learn about Catalyst framework, developed with focus on reproducibility, fast experimentation, and code/idea reusing.

https://bit.ly/2Yah4r7

Using AI to predict retinal disease progression

In this post, you will learn about AI system that can predict the development of exAMD which was created by DeepMind in collaboration with Moorfields Eye Hospital and Google Health.

https://bit.ly/2MEbnwv

How to Do Data Exploration for Image Segmentation and Object Detection

In this article, the author will share with you how he approaches data exploration for image segmentation and object detection problems.

https://bit.ly/3cL9IQf

Data Science Digest (June 2020)

Hi folks, I’m happy to share with you the latest Data Science Digest issue featuring Data Science & Machine Learning goodies for June 2020. Please upvote on Habr and applaud on Medium.

Habr (RU) — https://bit.ly/30lGGUR
Medium (EN) — https://bit.ly/3f4xbNS

The Ultimate Guide to Deploying Machine Learning Models

This multi-part series is a great resource for learning about model deployment. Covers a variety of topics, including common pitfalls, interfaces, model registries, A/B testing and more.

https://bit.ly/3cNSGB3

OpenCV Social Distancing Detector

In this tutorial, you will learn what social distancing is and how OpenCV and deep learning can be used to implement a social distancing detector.

https://bit.ly/2ULzAWb