Top KDnuggets tweets, Mar 13-19: Top R Packages for Data Cleaning; How to solve 90% of #NLP problems
Tags: Top tweets
Also: Artificial Neural Networks Optimization using Genetic Algorithm with Python; How To Work In Data Science, AI, Big Data; Why #BERT has 3 Embedding Layers and Their Implementation Details #DeepLearning; How to Train a Keras Model 20x Faster with a TPU for Free
Most popular @KDnuggets tweets for Mar 13 - 19 were:
Most Retweeted, Favorited & Viewed:
Top R Packages for Data Cleaning https://t.co/TmiEKpneCa https://t.co/9Od1rf3VCu
Most Clicked:
How to solve 90% of #NLP problems: a step-by-step guide #KDN https://t.co/CdXbIDk9te
Top 10 most engaging Tweets
Most Retweeted, Favorited & Viewed:
Top R Packages for Data Cleaning https://t.co/TmiEKpneCa https://t.co/9Od1rf3VCu
Most Clicked:
How to solve 90% of #NLP problems: a step-by-step guide #KDN https://t.co/CdXbIDk9te
Top 10 most engaging Tweets
- Top R Packages for Data Cleaning https://t.co/TmiEKpneCa https://t.co/9Od1rf3VCu
- Artificial Neural Networks Optimization using Genetic Algorithm with Python https://t.co/inp0UQTgzn https://t.co/eP9XFErd3M
- How To Work In Data Science, AI, Big Data https://t.co/ou55u1dtEQ https://t.co/zho8cimGkU
- Top R Packages for #DataCleaning #rstats https://t.co/TmiEKpneCa https://t.co/VWB9EuvUW2
- How to solve 90% of #NLP problems: a step-by-step guide #KDN https://t.co/CdXbIDk9te
- Why #BERT has 3 Embedding Layers and Their Implementation Details #DeepLearning https://t.co/XlSQ3ovCIv https://t.co/9YKJ64dsTs
- How to Train a Keras Model 20x Faster with a TPU for Free https://t.co/RKM7OPnMkO https://t.co/fCvUy2WhFk
- Real world implementation of Logistic Regression https://t.co/K3BlBpkZlh https://t.co/JHOMn3Y4P0
- Building NLP Classifiers Cheaply With #TransferLearning and Weak Supervision - a personal project to cheaply build a classifier to detect anti-semitic tweets, with no public dataset available @fastdotai #AI https://t.co/fsfI7dQ6dT https://t.co/4XxFjcC8TZ
- #Free Public Datasets #KDN https://t.co/I7kN6OJRDQ
