When it comes to choosing the best deep learning framework, PyTorch and TensorFlow are the undisputed champions and often the most preferred choice among machine learning engineers. Both are quite powerful, open-source, have huge libraries, are backed by tech giants like Facebook and Google, and are widely used in AI and machine learning research and production.
According to a Stackoverflow survey, these are the most widely used frameworks among others, with PyTorch enjoying a market share of approximately 10.6% and TensorFlow having 10.1%.
PyTorch is mostly preferred for research work because of how simple it is to use and offers dynamic computation graphs. This is particularly highly beneficial in debugging and experimentation. Having more Pythonic feel, it can be seamlessly integrated with popular Python tools and libraries. Therefore, it becomes an excellent choice for prototyping and academic work.
On the other hand, TensorFlow is great for a production environment. It offers tools like TensorFlow Serving, TensorFlow Lite, and TensorFlow.js that make deployment easier. It also has a static computation graph, which makes performance optimization easier, which is necessary for large-scale deployments.
The popular AI and machine learning certifications often provide deep understanding of these widely used frameworks along with their applications in AI and ML projects for a practical understanding of these frameworks.
While choosing the right one might be difficult for you, the following infographic explores their pros and cons, applications, and use cases, which will help you make a better decision.
