@@ -22,10 +22,6 @@ as our running example. The network will have four parameters, and will be train
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gradient descent to fit random data by minimizing the Euclidean distance
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between the network output and the true output.
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- .. note ::
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- You can browse the individual examples at the
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- :ref: `end of this page <examples-download >`.
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-
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.. contents :: Table of Contents
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:local:
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@@ -215,73 +211,3 @@ times when defining the forward pass.
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We can easily implement this model as a Module subclass:
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.. includenodoc :: /beginner/examples_nn/dynamic_net.py
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-
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- .. _examples-download :
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-
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- Examples
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- ~~~~~~~~
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-
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- You can browse the above examples here.
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-
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- Tensors
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- -------
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-
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- .. toctree ::
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- :maxdepth: 2
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- :hidden:
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-
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- /beginner/examples_tensor/polynomial_numpy
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- /beginner/examples_tensor/polynomial_tensor
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-
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- .. galleryitem :: /beginner/examples_tensor/polynomial_numpy.py
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- .. galleryitem :: /beginner/examples_tensor/polynomial_tensor.py
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-
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- .. raw :: html
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-
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- <div style =' clear :both ' ></div >
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-
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- Autograd
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- --------
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-
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- .. toctree ::
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- :maxdepth: 2
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- :hidden:
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-
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- /beginner/examples_autograd/polynomial_autograd
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- /beginner/examples_autograd/polynomial_custom_function
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-
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- .. galleryitem :: /beginner/examples_autograd/polynomial_autograd.py
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- .. galleryitem :: /beginner/examples_autograd/polynomial_custom_function.py
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- .. raw :: html
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-
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- <div style =' clear :both ' ></div >
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-
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- ``nn `` module
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- --------------
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-
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- .. toctree ::
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- :maxdepth: 2
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- :hidden:
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- /beginner/examples_nn/polynomial_nn
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- /beginner/examples_nn/polynomial_optim
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- /beginner/examples_nn/polynomial_module
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- /beginner/examples_nn/dynamic_net
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- .. galleryitem :: /beginner/examples_nn/polynomial_nn.py
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- .. galleryitem :: /beginner/examples_nn/polynomial_optim.py
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- .. galleryitem :: /beginner/examples_nn/polynomial_module.py
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- .. galleryitem :: /beginner/examples_nn/dynamic_net.py
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- .. raw :: html
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- <div style =' clear :both ' ></div >
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