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Welcome to PyTorch Tutorials

To get started with learning PyTorch, start with our Beginner Tutorials. The :doc:`60-minute blitz </beginner/deep_learning_60min_blitz>` is the most common starting point, and gives you a quick introduction to PyTorch. If you like learning by examples, you will like the tutorial :doc:`/beginner/pytorch_with_examples`

If you would like to do the tutorials interactively via IPython / Jupyter, each tutorial has a download link for a Jupyter Notebook and Python source code.

We also provide a lot of high-quality examples covering image classification, unsupervised learning, reinforcement learning, machine translation and many other applications at https://github.com/pytorch/examples/

You can find reference documentation for PyTorch's API and layers at http://docs.pytorch.org or via inline help. If you would like the tutorials section improved, please open a github issue here with your feedback: https://github.com/pytorch/tutorials

Beginner Tutorials

.. customgalleryitem::
   :figure: /_static/img/thumbnails/pytorch-logo-flat.png
   :tooltip: Understand PyTorch’s Tensor library and neural networks at a high level.
   :description: :doc:`/beginner/deep_learning_60min_blitz`


.. customgalleryitem::
   :tooltip: Understand similarities and differences between torch and pytorch.
   :figure: /_static/img/thumbnails/torch-logo.png
   :description: :doc:`/beginner/former_torchies_tutorial`


.. customgalleryitem::
   :tooltip: This tutorial introduces the fundamental concepts of PyTorch through self-contained examples.
   :figure: /_static/img/thumbnails/examples.png
   :description: :doc:`/beginner/pytorch_with_examples`


.. galleryitem:: beginner/transfer_learning_tutorial.py


.. galleryitem:: beginner/data_loading_tutorial.py


.. customgalleryitem::
    :tooltip: I am writing this tutorial to focus specifically on NLP for people who have never written code in any deep learning framework
    :figure: /_static/img/thumbnails/babel.jpg
    :description: :doc:`/beginner/deep_learning_nlp_tutorial`


.. toctree::
   :maxdepth: 2
   :hidden:
   :includehidden:
   :caption: Beginner Tutorials

   beginner/deep_learning_60min_blitz
   beginner/former_torchies_tutorial
   beginner/pytorch_with_examples
   beginner/transfer_learning_tutorial
   beginner/data_loading_tutorial
   beginner/deep_learning_nlp_tutorial

Intermediate Tutorials

RNNs for NLP

Applying recurrent neural networks to natural language tasks, from classification to generation.

.. galleryitem:: intermediate/char_rnn_classification_tutorial.py

.. galleryitem:: intermediate/char_rnn_generation_tutorial.py
  :figure: _static/img/char_rnn_generation.png

.. galleryitem:: intermediate/seq2seq_translation_tutorial.py
  :figure: _static/img/seq2seq_flat.png

Reinforcement Learning

.. galleryitem:: intermediate/reinforcement_q_learning.py
    :figure: _static/img/cartpole.gif


.. toctree::
   :maxdepth: 2
   :includehidden:
   :hidden:
   :caption: Intermediate Tutorials

   intermediate/char_rnn_classification_tutorial
   intermediate/char_rnn_generation_tutorial
   intermediate/seq2seq_translation_tutorial
   intermediate/reinforcement_q_learning

Advanced Tutorials

.. galleryitem:: advanced/neural_style_tutorial.py
    :intro: This tutorial explains how to impletment the Neural-Style algorithm developed by Leon A. Gatys, Alexander S. Ecker and Matthias Bethge.


.. galleryitem:: advanced/numpy_extensions_tutorial.py


.. galleryitem:: advanced/super_resolution_with_caffe2.py


.. customgalleryitem::
   :tooltip: Implement custom extensions in C.
   :description: :doc:`/advanced/c_extension`



.. toctree::
   :maxdepth: 2
   :includehidden:
   :hidden:
   :caption: Advanced Tutorials

   advanced/neural_style_tutorial
   advanced/numpy_extensions_tutorial
   advanced/super_resolution_with_caffe2
   advanced/c_extension