There are so many deep learning frameworks out there. How are you supposed to know which one to use?
That’s why I am going to compare ten of the most popular deep learning frameworks, analyzing the merits and drawbacks of each. Using code, programmatic features, and theory, I’ll navigate this field to draw some clear, reasonable conclusions.
Out of all the deep learning frameworks, TensorFlow is hands-down the most popular in terms of developer activity on GitHub. Google created it to help power almost all of its massively scaled services (like Gmail) to translate then open source it for the rest of us. In the world today, brands like Uber Airbnb and Dropbox have all decided to leverage this framework for their own services.
Currently its best supported client language is Python, but there are also experimental interfaces available in C++ Java and Go. Because it’s so popular it has bindings for other languages, like C sharp and Giulia, created by the open source community.
Having such a massive developer community, TensorFlow has rich detailed documentation not only from its official website but from various third-party sources from around the web. This documentation covers its various features, like TensorBoard. TensorBoard lets developers monitor the model training process via various visualizations and it’s a crucial part of its suite another crucial part is tension flow serving which allows developers to easily serve their models at scale in a production environment and includes distributed training TensorFlow Light even enables on device inference with low latency for mobile phones. But despite all of this TensorFlow is pretty low level. You have to specify a lot of magic numbers like the number of layers in your network, the dimension of your input data — and this requires a lot of boilerplate coding on the developers part which can be tedious and difficult.
Ready to go deeper? Watch the full video below where we’ll go over PyTorch, MXNet, Chainer, CNTK, Sonnet, DeepLearning4J, CoreML, and ONNX — among others.