Towards Data Science: Estimating the Global Growth of Coronavirus
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According to the COVID-19 data aggregated by John Hopkins, there are now over 200 thousand cases of coronavirus globally. And from what it…Continue reading on Towards Data Science »

Towards Data Science: Fine-Tuning the Strategy Using a Particle Swarm Optimization
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Towards Data Science: Don’t learn machine learning
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Towards Data Science: Berlin House Rental Market Analysis
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Am I paying a fair price?Continue reading on Towards Data Science »

Towards Data Science: Knowledge Graphs for Social Good Workshop: Helping the United Nations achieve Sustainable…
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The UN Sustainable Development Goals (SDGs), set in 2015, are a collection of 17 shared global goals intended to improve the health and…Continue reading on Towards Data Science »

Towards Data Science: The Hottest Topics in Machine Learning
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Understand Topic Modeling and Latent Dirichlet Allocation (LDA) in PythonContinue reading on Towards Data Science »

Towards Data Science: The Basics: Time Series and Seasonal Decomposition
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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
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Elevate your data science projects by following these variables’ mapContinue reading on Towards Data Science »

Towards Data Science: 7 Habits of Highly Effective Programmers
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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