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The State of Machine Learning Frameworks in 2019

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Since deep learning regained prominence in 2012, many machine learning frameworks have clamored to become the new favorite among researchers and industry practitioners. From the early academic outputs Caffe and Theano to the massive industry-backed PyTorch and TensorFlow, this deluge of options makes it difficult to keep track of what the most popular frameworks actually are. If you only browsed Reddit, you might assume that everyone's switching to PyTorch. Judging instead by Francois Chollet's Twitter, TensorFlow/Keras may appear as the dominant framework while PyTorch's momentum is stalling. In 2019, the war for ML frameworks has two remaining main contenders: PyTorch and TensorFlow. My analysis suggests that researchers are abandoning TensorFlow and flocking to PyTorch in droves.


The State of Machine Learning Frameworks in 2019

#artificialintelligence

Since deep learning regained prominence in 2012, many machine learning frameworks have clamored to become the new favorite among researchers and industry practitioners. From the early academic outputs Caffe and Theano to the massive industry-backed PyTorch and TensorFlow, this deluge of options makes it difficult to keep track of what the most popular frameworks actually are. If you only browsed Reddit, you might assume that everyone's switching to PyTorch. Judging instead by Francois Chollet's Twitter, TensorFlow/Keras may appear as the dominant framework while PyTorch's momentum is stalling. In 2019, the war for ML frameworks has two remaining main contenders: PyTorch and TensorFlow. My analysis suggests that researchers are abandoning TensorFlow and flocking to PyTorch in droves.


PyTorch and TensorFlow: Which ML Framework is More Popular in Academia and Industry

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Horace He recently published an article summarising The State of Machine Learning Frameworks in 2019. The article utilizes several metrics to argue the point that PyTorch is quickly becoming the dominant framework for research, whereas TensorFlow is the dominant framework for applications deployed within a commercial/industrial context. He, a research student at Cornell University, counted the number of papers discusing either PyTorch or TensorFlow that were presented at a series of well-known machine learning oriented conferences, namely ECCV, NIPS, ACL, NAACL, ICML, CVPR, ICLR, ICCV and EMNLP. In summary, the majority of papers were implemented in PyTorch for every major conference in 2019. PyTorch outnumbered TensorFlow by 2:1 in vision related conferences and 3:1 in language related conferences.


Why You Should Start Your Deep Learning Journey With PyTorch

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There's no denying the fact that Deep Learning as we know it, how awesome it is when we can see that with minimal or no human-intervention a job can be done. Since, Machine Learning, Deep Learning is dubbed to be one of the sexiest jobs of the 21st century(hyped?) so there has to be some starting point, a sort of a roadmap that you can follow to reach to the other side. Luckily, we can now approach it relatively easier with modern frameworks like Tensorflow, PyTorch which gives you a high-level interface to build awesome stuff! Let's discuss why you should start with PyTorch. That means line-by-line execution of the code and simultaneous building of the computation graphs just like in python.


Interested in machine learning? Better learn PyTorch

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Building on the rampant popularity of Python was always going to be a good idea for the Facebook-born PyTorch, an open source machine learning framework. Just how good of an idea, however, few could have guessed. That's because no matter how many things you get right when launching an open source project (great docs, solid technical foundation, etc.), there is always an element of luck to a project's success. Well, consider PyTorch lucky, then. Because it's booming and, if analyst Thomas Dinsmore is to be believed, "By the end of [2020] PyTorch will have more active contributors than TensorFlow."