For these cases, PyTorch and TensorFlow can be quite effective, especially if there is already a trained model similar to what you need in the framework’s model library. PyTorch.

PyTorch often shines with its simple syntax and approachable learning curve. TensorFlow has improved with its eager execution mode, making it more accessible for newcomers. Performance ...

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 ...

Is PyTorch better than TensorFlow for general use cases? originally appeared on Quora: the place to gain and share knowledge, empowering people to learn from others and better understand the world ...

PyTorch is still growing, while TensorFlow’s growth has stalled. Graph from StackOverflow trends . StackOverflow traffic for TensorFlow might not be declining at a rapid speed, but it’s ...

Putting TensorFlow back in PyTorch, back in TensorFlow (with differentiable TensorFlow PyTorch adapters). Do you have a codebase that uses TensorFlow and one that uses PyTorch and want to train a ...

PyTorch vs TensorFlow. While TensorFlow is the workhorse of Google’s ML efforts, it’s not the only open-source ML training library. In recent years the open-source PyTorch framework, ...