Here's a concise explanation of the origin story differences between TensorFlow and PyTorch: TensorFlow: Developed by Google primarily for internal use in their machine learning projects. Later ...

Learn about the unique features of TensorFlow and PyTorch, two popular frameworks for machine learning and deep learning, and how they compare and contrast with each other.

Both TensorFlow and PyTorch are great tools that make data scientist’s lives easier and better. When it comes to choosing the better one, it is all about the desired effect to be delivered. TensorFlow ...

Unlike TensorFlow, PyTorch hasn’t experienced any major ruptures in the core code since the deprecation of the Variable API in version 0.4. (Previously, Variable was required to use autograd ...

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 ...

TensorFlow: It was developed at Google Brain and released in 2015. Since then, rapid popularity supported by a strong ecosystem as well as production-level deployment support has grown. TensorFlow is ...

TensorFlow shines when it comes to deploying models in production. Its suite of tools contains TensorFlow Serving for high-scale model serving, TensorFlow Lite for deploying models to mobile formats, ...

PyTorch defines a class called Tensor (torch.Tensor) to store and operate on homogeneous multidimensional rectangular arrays of numbers. PyTorch Tensors are similar to NumPy Arrays, but can also be ...

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 ...

An exercise in implementing the same CNN architecture in both PyTorch and Tensorflow. I have tried to keep the architecture, optimizer, learning rate, and scheduler the same across both implementation ...