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Update links for tutorials in README (#1123)

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Du Phan 2017-03-28 21:21:40 +09:00 committed by Adam Paszke
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@ -135,11 +135,11 @@ Writing new neural network modules, or interfacing with PyTorch's Tensor API was
and with minimal abstractions.
You can write new neural network layers in Python using the torch API
[or your favorite numpy based libraries such as SciPy](https://github.com/pytorch/tutorials/blob/master/Creating%20extensions%20using%20numpy%20and%20scipy.ipynb).
[or your favorite numpy based libraries such as SciPy](http://pytorch.org/tutorials/advanced/numpy_extensions_tutorial.html).
If you want to write your layers in C/C++, we provide an extension API based on
[cffi](http://cffi.readthedocs.io/en/latest/) that is efficient and with minimal boilerplate.
There is no wrapper code that needs to be written. [You can see an example here](https://github.com/pytorch/extension-ffi).
[cffi](http://cffi.readthedocs.io/en/latest/) that is efficient and with minimal boilerplate.
There is no wrapper code that needs to be written. You can see [a tutorial here](http://pytorch.org/tutorials/advanced/c_extension.html) and [an example here](https://github.com/pytorch/extension-ffi).
## Installation
@ -210,7 +210,7 @@ should increase shared memory size either with --ipc=host or --shm-size command
## Getting Started
Three pointers to get you started:
- [Tutorials: notebooks to get you started with understanding and using PyTorch](https://github.com/pytorch/tutorials)
- [Tutorials: get you started with understanding and using PyTorch](http://pytorch.org/tutorials/)
- [Examples: easy to understand pytorch code across all domains](https://github.com/pytorch/examples)
- The API Reference: [http://pytorch.org/docs/](http://pytorch.org/docs/)