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pytorch 包教不包会
| Date | Stars |
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| 2026-07-24 | 394 |
| 2026-07-25 | 394 |
| 2026-07-28 | 394 |
| 2026-07-30 | 394 |
| 2026-08-06 | 394 |
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# pytorch 包教不包会 pytorch-tutorial-zh 雏鹰起飞部分是为了能够快速上手PyTorch 小试牛刀部分则是用来练手的模型，大部分都是对论文工作的复现，或者是一些有意思的例子。 为了避免 jupyter notebook 加载过慢，可以直接选择看 .py 文件，代码和 notebook 中基本一样，只是少了一些图示说明罢了。 ## 如何安装Pytorch? 安装 Pytorch 0.4.0版本，linux、windows 下可以通过这个 [Pytorch官网](http://pytorch.org/) ## 一、雏鹰起飞 | Content | .ipynb 文件 | .py 文件 | | ------------------ | :---------------------: | :--------------------------: | | 1.Tensor基础 | [Tensor基础.ipynb](./basis/1、Tensor基础.ipynb) | [Tensor基础.py](./basis/py/tensor_basis.py) | | 2.autograd机制 | [autograd机制.ipynb](./basis/2、autograd机制.ipynb) | [autograd机制.py](./basis/py/autograd.py) | | 3.线性回归 | [线性回归.ipynb](./basis/3、线性回归.ipynb) | [线性回归.py](./basis/py/linear_regression.py) | | 4.多层感知机 | [多层感知机.ipynb](./basis/4、多层感知机.ipynb) | [多层感知机.py](./basis/py/mlp.py) | | 5.Dataset和DataLoader | [Dataset和DataLoader.ipynb](./basis/5、Dataset和DataLoader.ipynb) | [Dataset和DataLoader.py](./basis/py/dataset.py) | | 6.CNN和MNIST | [CNN和MNIST.ipynb](./basis/CNN和MNIST.ipynb) | [CNN和MNIST.py](./basis/py/simplecnn.py) | | 7.参数初始化和使用预训练模型 | [参数初始化和使用预训练模型.ipynb](./basis/参数初始化和使用预训练模型.ipynb) | [参数初始化和使用预训练模型.py](./basis/py/pretrain.py) | | 8.模型微调的各种trick | [模型微调的各种trick.ipynb](./basis/模型微调的各种trick.ipynb) | [模型微调的各种trick.py](./basis/py/fine_tune.py) | | 9.模型保存和加载 | [模型保存和加载.ipynb](./basis/模型保存和加载.ipynb) | [模型保存和加载.py](./basis/py/save_load.py) | | 10.循环神经网络（RNN） | [循环神经网络（RNN）.ipynb](./basis/rnn.ipynb) | [循环神经网络（RNN）.py](./basis/py/rnn.py) | ## 二、小试牛刀 ### 1、计算机视觉——经典算法（卷积神经网络专区） | Content | .ipynb 文件 | .py 文件 | paper | | ------------------ | :---------------------: | :--------------------------: |:--------------------------: | | AlexNet | [AlexNet.ipynb](./CV/AlexNet.ipynb) | [AlexNet.py](./CV/py/AlexNet.py) | [AlexNet paper](https://tinyurl.com/j4pu2rc) | | VGG | [VGG.ipynb](./CV/VGG.ipynb) | [VGG.py](./CV/py/VGG.py) | [VGG paper](https://arxiv.org/abs/1409.1556) | | Network In Network | [NIN.ipynb](./CV/NIN.ipynb) | [NIN.py](./CV/py/NIN.py) | [Network In Network paper](https://arxiv.org/abs/1312.4400) | | GoogleNet | [GoogleNet.ipynb] | [GoogleNet] | [GoogleNet V1 paper](https://arxiv.org/abs/1409.4842) | | ResNet | [ResNet.ipynb](./CV/ResNet.ipynb) | [ResNet.py](./CV/py/ResNet.py) | [ResNet paper](https://arxiv.org/abs/1512.03385) | | DenseNet | [DenseNet.ipynb] | [DenseNet] | [DenseNet paper](https://arxiv.org/abs/1608.06993) | ### 2、计算机视觉——应用领域 | Content | .ipynb 文件 | .py 文件 | paper | | ------------------ | :---------------------: | :--------------------------: |:--------------------------: | | 语义分割(FCN) | [FCN.ipynb](./CV/FCN.ipynb) | [FCN.py](./CV/py/FCN.py) | [FCN paper](https://arxiv.org/abs/1411.4038) | ### 3、自然语言处理 | Content | .ipynb 文件 | .py 文件 | paper | | ------------------ | :---------------------: | :--------------------------: |:--------------------------: | | Word2Vec | [Word2Vec.ipynb](./NLP/Word2Vec.ipynb) | [Word2Vec.py](./NLP/py/word2vec.py) | [Word2Vec Toolkit](https://code.google.com/archive/p/word2vec/) | | LSTM | [使用LSTM来生成周杰伦歌词.ipynb](./NLP/LSTM.ipynb) | [使用LSTM来生成周杰伦歌词.py](./NLP/py/lstm.py) | [paper] | | Encoder-Decoder | [使用Encoder-Decoder来完成机器翻译.ipynb](./NLP/encode_decoder.ipynb) | [Encoder-Decoder.py](./NLP/py/encoder_decoder.py) | [paper] | | 注意力机制 | [使用Encoder-Decoder + Attention 机制来完成机器翻译.ipynb](./NLP/attention.ipynb) | [Attention.py](./NLP/py/attention.py) | [Neural Machine Translation by Jointly Learning to Align and Translate](https://arxiv.org/abs/1409.0473) | ### 4、生成模型 | Content | .ipynb 文件 | .py 文件 | paper | | ------------------ | :---------------------: | :--------------------------: |:--------------------------: | | GAN | [GAN.ipynb](./GAN/GAN.ipynb) | [GAN.py](./GAN/py/GAN.py) | [GAN paper](https://arxiv.org/abs/1406.2661) | | DCGAN | [DCGAN.ipynb](./GAN/DCGAN.ipynb) | [DCGAN.py](./GAN/py/DCGAN.py) | [DCGAN paper](https://arxiv.org/abs/1511.06434) | | Variational Auto-Encoder | [VAE.ipynb]() | [VAE.py](./GAN/py/VAE.py) |
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:8c2e17e90dff6a73, topic:deep-learning, topic:pytorch, readme:autograd
matched fp:8c2e17e90dff6a73, topic:gan
matched fp:8c2e17e90dff6a73, topic:tutorial, readme:tutorial