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Skorch Notebooks Readme Md At Master Github

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skorch notebooks readme md at master github

There was an error while loading. Please reload this page. A scikit-learn compatible neural network library that wraps PyTorch. To see more elaborate examples, look here. skorch also provides many convenient features, among others: You need a working conda installation.

Get the correct miniconda for your system from here. To install skorch, you need to use the conda-forge channel: There was an error while loading. Please reload this page. The following are examples and notebooks on how to use skorch. © Copyright 2017, Marian Tietz, Daniel Nouri, Benjamin Bossan.

Revision e32c195a. pip install skorch Copy PIP instructions scikit-learn compatible neural network library for pytorch A scikit-learn compatible neural network library that wraps PyTorch. To see more elaborate examples, look here. skorch also provides many convenient features, among others:

A scikit-learn compatible neural network library that wraps PyTorch. The goal of skorch is to make it possible to use PyTorch with sklearn. This is achieved by providing a wrapper around PyTorch that has an sklearn interface. In that sense, skorch is the spiritual successor to nolearn, but instead of using Lasagne and Theano, it uses PyTorch. skorch does not re-invent the wheel, instead getting as much out of your way as possible. If you are familiar with sklearn and PyTorch, you don’t have to learn any new concepts, and the syntax should be well known.

(If you’re not familiar with those libraries, it is worth getting familiarized.) Additionally, skorch abstracts away the training loop, making a lot of boilerplate code obsolete. A simple net.fit(X, y) is enough. Out of the box, skorch works with many types of data, be it PyTorch Tensors, NumPy arrays, Python dicts, and so on. However, if you have other data, extending skorch is easy to allow for that. Overall, skorch aims at being as flexible as PyTorch while having a clean interface as sklearn.

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There Was An Error While Loading. Please Reload This Page.

There was an error while loading. Please reload this page. A scikit-learn compatible neural network library that wraps PyTorch. To see more elaborate examples, look here. skorch also provides many convenient features, among others: You need a working conda installation.

Get The Correct Miniconda For Your System From Here. To

Get the correct miniconda for your system from here. To install skorch, you need to use the conda-forge channel: There was an error while loading. Please reload this page. The following are examples and notebooks on how to use skorch. © Copyright 2017, Marian Tietz, Daniel Nouri, Benjamin Bossan.

Revision E32c195a. Pip Install Skorch Copy PIP Instructions Scikit-learn Compatible

Revision e32c195a. pip install skorch Copy PIP instructions scikit-learn compatible neural network library for pytorch A scikit-learn compatible neural network library that wraps PyTorch. To see more elaborate examples, look here. skorch also provides many convenient features, among others:

A Scikit-learn Compatible Neural Network Library That Wraps PyTorch. The

A scikit-learn compatible neural network library that wraps PyTorch. The goal of skorch is to make it possible to use PyTorch with sklearn. This is achieved by providing a wrapper around PyTorch that has an sklearn interface. In that sense, skorch is the spiritual successor to nolearn, but instead of using Lasagne and Theano, it uses PyTorch. skorch does not re-invent the wheel, instead getting as...

(If You’re Not Familiar With Those Libraries, It Is Worth

(If you’re not familiar with those libraries, it is worth getting familiarized.) Additionally, skorch abstracts away the training loop, making a lot of boilerplate code obsolete. A simple net.fit(X, y) is enough. Out of the box, skorch works with many types of data, be it PyTorch Tensors, NumPy arrays, Python dicts, and so on. However, if you have other data, extending skorch is easy to allow for ...