This PyTorch vs TensorFlow guide will provide more insight into both but each offers a powerful platform for designing and deploying machine learning models.

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.

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

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

Both PyTorch and TensorFlow support deep learning and transfer learning. Transfer learning, which is sometimes called custom machine learning, starts with a pre-trained neural network model and ...

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 ...

First is PyTorch, with its tremendous following and mindshare. If you look at the metrics alone it might be easy to miss, but PyTorch is quite possibly the most used and talked about deep learning ...

TensorFlow uses a dataflow graph to represent computations. It shares this space with another open-source machine-learning framework called PyTorch.

TensorFlow is Google's open-source AI framework used for machine learning and deep learning applications.