KDnuggets Home » News » 2018 » Mar » Top Stories, Tweets » Top February Stories: Neural network AI is simple. So… Stop pretending you are a genius ( 18:n10 )

Top February Stories: Neural network AI is simple. So… Stop pretending you are a genius


Also: Top 20 Python AI and Machine Learning Open Source Projects, A Tour of The Top 10 Algorithms for Machine Learning Newbies; 8 Neural Network Architectures Machine Learning Researchers Need to Learn.



On Feb 7, 2018, LinkedIn unexplainably decided to no longer display the number of LinkedIn shares, and all the APIs for LinkedIn share counts now return zero. This is a pity, since LinkedIn shares have typically been 40-60% of all social shares on KDnuggets, so the share counts you see now are about half of the actual counts.

We can still recognize the most popular posts based on the number of unique page views (UPV) - not changed and the remaining social share counts - mainly Facebook, Twitter, and AddThis.

Platinum BlogMost Viewed - Platinum Badge (>24,000 UPV)

  1. Neural network AI is simple. So... Stop pretending you are a genius, by Brandon Wirtz (*)


Gold BlogMost Viewed - Gold Badges (>12,000 UPV)

  1. Top 20 Python AI and Machine Learning Open Source Projects, by Ilan Reinstein
  2. A Tour of The Top 10 Algorithms for Machine Learning Newbies, by James Le


Silver BlogMost Viewed - Silver Badges (> 6,000 UPV)

  1. 5 Fantastic Practical Machine Learning Resources, by Matthew Mayo
  2. A Comparative Analysis of Top 6 BI and Data Visualization Tools in 2018, by Igor Bobriakov (*)
  3. Gainers and Losers in Gartner 2018 Magic Quadrant for Data Science and Machine Learning Platforms, by Gregory Piatetsky (*)
  4. Introduction to Functional Programming in Python, by Spiro Sideris
  5. The 8 Neural Network Architectures Machine Learning Researchers Need to Learn, by James Le
  6. 5 Machine Learning Projects You Should Not Overlook, by Matthew Mayo (*)
  7. Data Science at the Command Line: Exploring Data, by Matthew Mayo
  8. Introduction to Python Ensembles, by Sebastian Flennerhag (*)
  9. Logistic Regression: A Concise Technical Overview, by Reena Shaw (*)
  10. A Simple Starter Guide to Build a Neural Network, by Jeff Hu
  11. Web Scraping Tutorial with Python: Tips and Tricks, by Jekaterina Kokatjuhha
  12. Want a Job in Data? Learn This, by Josh Devlin (*)



For the most shared posts, we had to reduce the thresholds because of LinkedIn removing their counts.

Platinum BlogMost Shared - Platinum Badge (>1200 shares)

  1. Neural network AI is simple. So... Stop pretending you are a genius, by Brandon Wirtz


Gold BlogMost Shared - Gold Badges (>600 shares)

  1. Top 20 Python AI and Machine Learning Open Source Projects, by Ilan Reinstein
  2. The 8 Neural Network Architectures Machine Learning Researchers Need to Learn, by James Le (*)
  3. 5 Fantastic Practical Machine Learning Resources, by Matthew Mayo


Silver BlogMost Shared - Silver Badges (>300 shares)

  1. Web Scraping Tutorial with Python: Tips and Tricks, by Jekaterina Kokatjuhha
  2. Introduction to Python Ensembles, by Sebastian Flennerhag
  3. Deep Learning Development with Google Colab, TensorFlow, Keras & PyTorch, by Fuat Beser
  4. Introduction to Functional Programming in Python, by Spiro Sideris
  5. Gainers and Losers in Gartner 2018 Magic Quadrant for Data Science and Machine Learning Platforms, by Gregory Piatetsky
  6. A Tour of The Top 10 Algorithms for Machine Learning Newbies, by James Le
  7. Data Science at the Command Line: Exploring Data, by Matthew Mayo
  8. A Basic Recipe for Machine Learning, by Hafidz Zulkifli (*)
  9. A Comparative Analysis of Top 6 BI and Data Visualization Tools in 2018, by Igor Bobriakov


(*) indicates that badge added or upgraded based on these monthly results.

Most Shareable (Viral) Blogs

Among the top blogs, here are the 5 blogs with the highest ratio of shares/unique views, which suggests that people who read it really liked it.
  1. How data science can improve retail, by Konrad Pabianczyk
  2. Top 15 Scala Libraries for Data Science in 2018, by Igor Bobriakov
  3. Understanding Learning Rates and How It Improves Performance in Deep Learning, by Hafidz Zulkifli
  4. 2018 Predictions for the Analytics & Data Science Hiring Market, by Linda Burtch
  5. The Birth of AI and The First AI Hype Cycle, by Alok Aggarwal