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 ...

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 ...

Like Google's TensorFlow, PyTorch is a library for the Python programming language — a favorite for machine learning and AI — that integrates with important Python add-ons like NumPy and data ...

He noted that Intel is doing work ranging from contributions at the language level with Python, partnering and optimizing the industry frameworks like PyTorch and TensorFlow, to releasing Intel ...

There are tools to convert Tensorflow, PyTorch, XGBoost, and LibSVM models into formats that CoreML and ML Kit understand. But other solutions try to provide a platform-agnostic layer for training ...