PyTorch-based code: Difference between revisions

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(Created page with "A PyTorch Code is a set of instructions written in the PyTorch Framework for implementing machine learning models, performing tensor operations, and conducting deep learning research. * <B>Context:</B> ** It can (typically) include PyTorch tensor operations for numerical computations. ** It can (typically) handle data loading and preprocessing using torch.utils.data.DataLoader. ** It can (typically) define neural network architectures using torch.nn...")
 
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A [[PyTorch Code]] is a set of instructions written in the [[PyTorch Framework]] for implementing machine learning models, performing tensor operations, and conducting deep learning research.
#REDIRECT [[PyTorch Code]]
* <B>Context:</B>
** It can (typically) include [[PyTorch tensor operations]] for numerical computations.
** It can (typically) handle data loading and preprocessing using [[torch.utils.data.DataLoader]].
** It can (typically) define [[neural network architectures]] using [[torch.nn.Module]].
** It can (often) utilize the [[autograd module]] for automatic differentiation.
** It can (often) be used for building, training, and evaluating various types of [[deep learning models]], including [[CNNs]], [[RNNs]], and [[transformers]].
** It can (often) be used to implement complex [[probabilistic models]] using library
** It can (often) involve creating [[custom training loops]] to control the training process.
** It can include [[custom loss functions]] to optimize model training.
** It can integrate with [[TorchScript]] for optimizing and deploying models in production environments.
** It can leverage [[GPU acceleration]] through [[CUDA]].
** It can perform [[distributed training]] to scale up model training across multiple devices or nodes.
** It can incorporate [[pre-trained models]] from the [[torchvision]] library for rapid development.
** It can use [[optimizers]] like [[SGD]], [[Adam]], and [[RMSprop]] from [[torch.optim]] to update model parameters.
** It can provide [[visualization]] of training metrics using tools like [[TensorBoardX]].
** It can support saving and loading models using [[torch.save]] and [[torch.load]].ies like [[Pyro]].
** It can be extended with custom [[C++ extensions]] for optimized performance.
** It can be tested and debugged interactively using [[Jupyter Notebooks]].
** It can interact with other [[Python libraries]] such as [[NumPy]], [[SciPy]], and [[Pandas]].
** It can (often) be used in combination with other [[deep learning frameworks]] for hybrid workflows.
** ...
* <B>Example(s):</B>
** a [[PyTorch-based image classification script]] that uses a [[Convolutional Neural Network (CNN)]] to classify images from the [[CIFAR-10 dataset]].
** a [[PyTorch-based natural language processing script]] implementing a [[Transformer Model]] for language translation.
** a [[PyTorch-based reinforcement learning script]] using [[Deep Q-Learning]] to train an agent in a simulated environment.
** a [[PyTorch-based time series prediction script]] using a [[Recurrent Neural Network (RNN)]] for forecasting future values.
** a [[PyTorch-based generative adversarial network (GAN) script]] for generating realistic images from random noise.
** ...
* <B>Counter-Example(s):</B>
** [[TensorFlow Code]], which is written using the [[TensorFlow Framework]].
** [[Keras Code]], which uses the [[Keras Framework]].
** [[scikit-learn scripts]], which are used for traditional machine learning tasks and not deep learning.
** ...
* <B>See:</B> [[PyTorch Framework]], [[TorchScript]], [[CUDA]], [[torch.nn.Module]], [[torch.optim]], [[torch.utils.data.DataLoader]], [[torchvision]], [[Pyro]], [[TensorBoardX]], [[Jupyter Notebooks]]
 
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== References ==
 
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__NOTOC__
[[Category:Concept]]
[[Category:Software and Technology]]
[[Category:Machine Learning]]
[[Category:Deep Learning]]

Latest revision as of 03:32, 13 July 2024

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