Difference between revisions of "PyTorch"
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* [http://www.marktechpost.com/2021/10/24/microsoft-ai-open-sources-pytorch-directml-a-package-to-train-machine-learning-models-on-gpus/ Microsoft AI Open-Sources ‘PyTorch-DirectML’: A Package To Train Machine Learning Models On GPUs | Asif Razzaq - Marketechpost] | * [http://www.marktechpost.com/2021/10/24/microsoft-ai-open-sources-pytorch-directml-a-package-to-train-machine-learning-models-on-gpus/ Microsoft AI Open-Sources ‘PyTorch-DirectML’: A Package To Train Machine Learning Models On GPUs | Asif Razzaq - Marketechpost] | ||
| − | PyTorch is a machine learning framework based on the Torch library, used for applications such as computer vision and natural language processing. It is free and open-source software released under the modified BSD license. PyTorch provides two high-level features: Tensor computation (like NumPy) with strong GPU acceleration and Deep [[Neural Network]]s built on a tape-based autograd system. It is written in Python and is relatively easy for most machine learning developers to learn and use. | + | PyTorch is a machine learning framework based on the Torch library, used for applications such as computer vision and natural language processing. It is free and open-source software released under the modified BSD license. PyTorch provides two high-level features: Tensor computation (like NumPy) with strong [[Processing Units - CPU, GPU, APU, TPU, VPU, FPGA, QPU|GPU]] acceleration and Deep [[Neural Network]]s built on a tape-based autograd system. It is written in Python and is relatively easy for most machine learning developers to learn and use. |
In PyTorch, the tape-based autograd system is a technique used to compute gradients efficiently and it happens to be used by backpropagation. Autograd is the core torch package for automatic differentiation1. A simple explanation of reverse-mode automatic differentiation can be found in this PyTorch forum post. PyTorch’s Autograd feature is part of what makes PyTorch flexible and fast for building machine learning projects. | In PyTorch, the tape-based autograd system is a technique used to compute gradients efficiently and it happens to be used by backpropagation. Autograd is the core torch package for automatic differentiation1. A simple explanation of reverse-mode automatic differentiation can be found in this PyTorch forum post. PyTorch’s Autograd feature is part of what makes PyTorch flexible and fast for building machine learning projects. | ||
Revision as of 07:40, 9 October 2023
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- PyTorch and TensorFlow: Which ML Framework is More Popular in Academia and Industry | Alex Giamas - InfoQ ...code used to generate the datasets and also interactive charts from the article | Horace He
- Microsoft AI Open-Sources ‘PyTorch-DirectML’: A Package To Train Machine Learning Models On GPUs | Asif Razzaq - Marketechpost
PyTorch is a machine learning framework based on the Torch library, used for applications such as computer vision and natural language processing. It is free and open-source software released under the modified BSD license. PyTorch provides two high-level features: Tensor computation (like NumPy) with strong GPU acceleration and Deep Neural Networks built on a tape-based autograd system. It is written in Python and is relatively easy for most machine learning developers to learn and use.
In PyTorch, the tape-based autograd system is a technique used to compute gradients efficiently and it happens to be used by backpropagation. Autograd is the core torch package for automatic differentiation1. A simple explanation of reverse-mode automatic differentiation can be found in this PyTorch forum post. PyTorch’s Autograd feature is part of what makes PyTorch flexible and fast for building machine learning projects.
