
Pytorch provides Tensors and Dynamic neural networks in Python with strong GPU acceleration. It offers following capabilities:
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Production Ready - Transition seamlessly between eager and graph modes with TorchScript, and accelerate the path to production with TorchServe.
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Distributed Training - Scalable distributed training and performance optimization in research and production is enabled by the torch.distributed backend.
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Robust Ecosystem- A rich ecosystem of tools and libraries extends PyTorch and supports development in computer vision, NLP and more.
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Cloud Support - PyTorch is well supported on major cloud platforms, providing frictionless development and easy scaling.
Official Website Link : https://pytorch.org/

Documentation
The official PyTorch Documentation containing docs, blogs, tutorials, and other resources.

VIDEO: Track Your PyTorch Machine Learning Experiments with W&B
Learn what you need to get started with PyTorch and W&B in 60 seconds

Using PyTorch with Weights & Biases
W&B provides first-class support for PyTorch, from logging gradients to profiling your code on the CPU and GPU.

How To Use GPU with PyTorch
A short tutorial on using GPUs for your deep learning models with PyTorch, from checking availability to visualizing GPU usage

Implementing Dropout in PyTorch: With Example
An example covering how to regularize your PyTorch model with Dropout, complete with code and interactive visualizations

How to Save and Load Models in PyTorch
Learn to correctly save and load your trained machine learning models in PyTorch.

Using LSTM in PyTorch: A Tutorial With Examples
A tutorial covering how to use LSTM in PyTorch, complete with code and interactive visualizations

Using the PyTorch Profiler with W&B
What really happens when you call .forward, .backward, and .step?

An Introduction To The PyTorch View Function
Demystify the View function in PyTorch and find a better way to design models.
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