TensorFlow is your ally for scalability and production. PyTorch is your friend for research flexibility and ease of use. The choice depends on your project needs, expertise, and long-term goals.

PyTorch recreates the graph on the fly at each iteration step. In contrast, TensorFlow by default creates a single data flow graph, optimizes the graph code for performance, and then trains the model.

When deploying large-scale deep learning applications, C++ may be a better choice than Python to meet application demands or to optimize model performance. Therefore, I specifically document my recent ...

PyTorch recreates the graph on the fly at each iteration step. In contrast, TensorFlow by default creates a single data flow graph, optimizes the graph code for performance, and then trains the model.

TensorFlow, PyTorch, Keras, Caffe, Microsoft Cognitive Toolkit, Theano and Apache MXNet are the seven most popular frameworks for developing AI applications.

Is PyTorch better than TensorFlow for general use cases? This question was originally answered on Quora by Roman Trusov.

Overview The right Python libraries can dramatically improve speed, efficiency, and maintainability in 2025 ...

It's possible to create neural networks from raw code. But there are many code libraries you can use to speed up the process. These libraries include Microsoft CNTK, Google TensorFlow, Theano, PyTorch ...