How to submit your own ideas for Data Science of the Day

Data Science of the Day is a fun way to share new, or perhaps even novel perspectives on data science related topics.

The criteria for an idea to be shared:

  • You must be logged into the forum. In the upper right, you can log in and you can even use Single-Sign On to create an account (just to make it all that much easier)
  • The topic must be related to data science in some fashion (this is quite broad including, but not limited to: loading, storing, and processing data, ML, DL, inferencing, ML Ops, there are lots of topics that are in play here)
  • A resource to share with the user (blog, research paper, video, etc…)
  • The novel thought about the topic (max ~280 characters)
  • (Optional) An interesting title (max ~140 characters)

If you have a suggestion, just reply to this thread with the information outlined and it will get taken into consideration for posting.

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Note: A new post will be made every business weekday, and it will be published at 9a in the Eastern Time zone.

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Trend Data and Charts to Satisfy Our Ever Increasing Dependence on Understanding What Other People are Doing. #FOMO
Build and Deploy Your XGBoost Model Using Jupyter and Algorithmia
Swish versus GELU. Which Activation Function Should You Choose for Image Classification and Why?
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CUDA Python: Work with CUDA Directly Using Python
Embed SQL Into Your R Code
Can Artificial Intelligence Be Used in Order to Create Artwork?
RAPIDAligner: Aligning Time Series at the Speed of Light
Creating a Real-Time License Plate Detection and Recognition App
An Introduction to GPU Accelerated Machine Learning in Python
An Introduction to GPU Accelerated Graph Processing in Python
Deep Learning for Cyber Security - Part 1
Deep Learning for Cyber Security - Part 2
An Introduction to GPU Accelerated Data Streaming in Python
The Best Keep Getting Better. HW and SW Improve SQL Performance
Directed Acyclic Graphs (DAGs) are Incredibly Important in Large Scale Data Processing. Want to Know How It Applies to Machine Learning?
Ever Think About Characterizing Signal Propagation to Close the Performance Gap in Unnormalized ResNets? Us to, Check out NFNets
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An Introduction to GPU Accelerated Machine Learning in Python
Embed Your SQL Query Into Your Python Code and Let It Rip on a GPU
The Billion Dollar AI Problem That Just Keeps Scaling
Peter Norvig Simulates an Economic Marketplace with Agent Interaction Using Python
An Introduction to GPU Accelerated Signal Processing in Python
An Example of Using a Variational Auto-Encoder (VAE) on Economic Data
How is Logistic Regression Related to Neural Networks?
An Introduction to Agent-based Models: Simulating Segregation with Python
A Fundamentally Novel and Faster Way of Performing One of The Most Basic Computations in Data Science
How AI Helps Prevent Cyberbullying
Can a Transformer Solve a Math Problem?
Multi-Node Multi-GPU (MNMG) Example on Azure Using Dask-CloudProvider
If You Could Speed Up a Spark Job Without Changing Your Code, Would You? RAPIDS Makes This a Reality
An Introduction to Distributed Computing with GPUs in Python
The Scikit-Learn Allows for Custom Estimators to Run on CPUs, GPUs and Multiple GPUs
There is Fast and Then There is Blazing Fast. Which Would You Rather Have on Google Colab?
Use Python to Build a Model to Classify Emotions in Acoustic Data
TRTorch is a PyTorch Deep Learning Optimizer to Run on GPUs
How Machine Learning is Changing Software: A Biased Overview
A Comparison of ML Experiment Tracking Tools
MADGRAD: A Best-of-Both-Worlds Optimizer with The Generalization Performance of SGD and at Least as Fast Convergence as That of Adam, Often Faster
The Bug Affecting Thousands of Pytorch Projects. Is One of Them Yours?
GPU-Accelerated SHAP for RAPIDS and XGboost is Now Here
Stop Struggling with Data Science Workflows
Don't Create Your Own Function If There is Already a Built-in Python Function for That Task
Exclusive Interview with Kaggle Notebooks Grandmaster Gabriel Preda
The Triton Inference Server Lets Teams Deploy Trained AI Models From Any Framework