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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.


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.


The Rise And Rise Of PyTorch

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Since the release of PyTorch in 2016, it is on a rollercoaster ride as its adoption among developers and researchers is continually increasing. Although it was released long after one of the most popular deep learning frameworks TensorFlow, over the years, PyTorch has quickly gained grounds and has overtaken advantage its competitors had due to their early release. Numerous organisations are utilising PyTorch in their business processes to innovate and eliminate various business challenges. More notably, Microsoft and Tesla have embraced PyTorch in their organisations for adding artificial intelligence capabilities. While Tesla has integrated it for autopilot in the car, Microsoft has been using it for internal developments and have also brought support on Azure.


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.


Keras or PyTorch as your first deep learning framework deepsense.ai

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So, you want to learn deep learning? Whether you want to start applying it to your business, base your next side project on it, or simply gain marketable skills – picking the right deep learning framework to learn is the essential first step towards reaching your goal. We strongly recommend that you pick either Keras or PyTorch. These are powerful tools that are enjoyable to learn and experiment with. We know them both from the teacher's and the student's perspective.