Originally published in Towards Data Science September 19, 2018
For today’s leading deep learning methods and technology, attend the conference and training workshops at Deep Learning World, June 16-19, 2019 in Las Vegas.
Deep learning continues to be the hottest thing in data science. Deep learning frameworks are changing rapidly. Just five years ago, none of the leaders other than Theano were even around.
I wanted to find evidence for which frameworks merit attention, so I developed this power ranking. I used 11 data sources across 7 distinct categories to gauge framework usage, interest, and popularity. Then I weighted and combined the data in this Kaggle Kernel.
UPDATE SEPT 20, 2018: Due to popular demand, I expanded the frameworks evaluated to include Caffe, Deeplearning4J, Caffe2, and Chainer. Now all deep learning frameworks with more than 1% reported usage on KDNuggets usage survey are included.
UPDATE SEPT 21, 2018: I made a number of methodological improvements in several of the metrics.
Without further ado, here are the Deep Learning Framework Power Scores:
While TensorFlow is the clear winner, there were some surprising findings. Let’s dive in!
The Contenders
All of these frameworks are open source. All except one work with Python, and some can work with R or other languages.
Continue reading this article here.
About the Author
Jeff Hale is into data science and machine learning.
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