Top KDnuggets tweets, Nov 08-14: Approaching (Almost) Any NLP Problem on #Kaggle; Choosing an Open Source #MachineLearning Library

Most popular @KDnuggets tweets for Nov 08-14 were

Most Retweeted:
Choosing an Open Source #MachineLearning Library: TensorFlow, Theano, Torch, scikit-learn, Caffe https://t.co/Dp9MupuUIj https://t.co/YVaD30hrRG



Most Favorited:
Approaching (Almost) Any NLP Problem on #Kaggle https://t.co/TtUBuMeZmW https://t.co/QpfvkM1obo

Most Viewed:
Approaching (Almost) Any NLP Problem on #Kaggle https://t.co/TtUBuMeZmW https://t.co/QpfvkM1obo

Most Clicked:
Approaching (Almost) Any NLP Problem on #Kaggle https://t.co/TtUBuMeZmW https://t.co/QpfvkM1obo

Top 10 most engaging Tweets

1. Approaching (Almost) Any NLP Problem on #Kaggle https://t.co/TtUBuMeZmW https://t.co/QpfvkM1obo
2. What is the difference between Bagging and Boosting? https://t.co/0WHOtydEHK #MachineLearning https://t.co/Qv5wBxTzXo
3. Which #Python package manager should you use? https://t.co/0xoQXxbK8T https://t.co/p6rTPN2lqw
4. The Practical Importance of Feature Selection https://t.co/yriNxYNkDW #MachineLearning https://t.co/qPHWcjl4V2
5. #MachineLearning tackles the big problems: Real-time #ASCIIArt Rendering Usinga Single Decision Tree https://t.co/wSqDeJplUS https://t.co/mmMy9s8CLv
6. #AlphaGoZero Explained In One Diagram https://t.co/WkkHdb1kix https://t.co/W5BAI2GhYw
7. How #Bayesian #Networks Are Superior in Understanding Effects of #Variables #DataScience #MachineLearning https://t.co/XdGyQvSmlV https://t.co/mDhrugw2OX
8. How (and why) to create a good validation set - fast.ai https://t.co/S2PcrWDkMr #MachineLearning https://t.co/OrrrQzZ497
9. Understanding #Hinton #Capsule Networks. Part I: Intuition. #DeepLearning #AI #ComputerVision https://t.co/7AGx7LyRhe https://t.co/53ZghAIwsX
10. When Will Demand for #DataScientist / #MachineLearning Experts Peak? #DataScience https://t.co/RBoniEpEuW https://t.co/ELxiIEn8zy