PyTorch1.x tutorials, examples and some books I found 【不定期更新】整理的PyTorch 1.x 最新版教程、例子和书籍
Note: some of these are old version; 下面的书籍部分还不是1.x版本。
该目录更新可能有延迟，全部资料请看该文件夹内文件
Part 1: Introduction to PyTorch and using tensors- Part 2: Building fully-connected neural networks with PyTorch
Part 3: How to train a fully-connected network with backpropagation on MNIST- Part 4: Exercise - train a neural network on Fashion-MNIST
Part 5: Using a trained network for making predictions and validating networks- Part 6: How to save and load trained models
Part 7: Load image data with torchvision, also data augmentation- Part 8: Use transfer learning to train a state-of-the-art image classifier for dogs and cats
Lecture 01_ Overview.pptx- Lecture 02 Linear Model.pptx
* Lecture 03 Gradient Descent.pptx
Lecture 04_ Back-propagation and PyTorch autograd.pptx- Lecture 05 Linear regression in PyTorch way.pptx
* Lecture 06 Logistic Regression.pptx
Lecture 07 Wide Deep.pptx- Lecture 08 DataLoader.pptx
* Lecture 09 Softmax Classifier.pptx
Lecture 10_ Basic CNN.pptx- Lecture 11 Advanced CNN.pptx
* Lecture 12 RNN.pptx
Lecture 13_ RNN II.pptx- Lecture 14 Seq2Seq.pptx
* Lecture 15 NSML, Smartest ML Platform.pptx
1-1-from-anns-to-deep-learning.pdf- 1-2-current-success.pdf
1-3-what-is-happening.pdf- 1-4-tensors-and-linear-regression.pdf
1-5-high-dimension-tensors.pdf- 1-6-tensor-internals.pdf
2-1-loss-and-risk.pdf- 2-2-overfitting.pdf
2-3-bias-variance-dilemma.pdf- 2-4-evaluation-protocols.pdf
2-5-basic-embeddings.pdf- 3-1-perceptron.pdf
3-2-LDA.pdf- 3-3-features.pdf
3-4-MLP.pdf- 3-5-gradient-descent.pdf
3-6-backprop.pdf- 4-1-DAG-networks.pdf
4-2-autograd.pdf- 4-3-modules-and-batch-processing.pdf
4-4-convolutions.pdf- 4-5-pooling.pdf
4-6-writing-a-module.pdf- 5-1-cross-entropy-loss.pdf
5-2-SGD.pdf- 5-3-optim.pdf
5-4-l2-l1-penalties.pdf- 5-5-initialization.pdf
5-6-architecture-and-training.pdf- 5-7-writing-an-autograd-function.pdf
6-1-benefits-of-depth.pdf- 6-2-rectifiers.pdf
6-3-dropout.pdf- 6-4-batch-normalization.pdf
6-5-residual-networks.pdf- 6-6-using-GPUs.pdf
7-1-CV-tasks.pdf- 7-2-image-classification.pdf
7-3-object-detection.pdf- 7-4-segmentation.pdf
7-5-dataloader-and-surgery.pdf- 8-1-looking-at-parameters.pdf
8-2-looking-at-activations.pdf- 8-3-visualizing-in-input.pdf
8-4-optimizing-inputs.pdf- 9-1-transposed-convolutions.pdf
9-2-autoencoders.pdf- 9-3-denoising-and-variational-autoencoders.pdf
9-4-NVP.pdf- 10-1-GAN.pdf
10-2-Wasserstein-GAN.pdf- 10-3-conditional-GAN.pdf
10-4-persistence.pdf- 11-1-RNN-basics.pdf
11-2-LSTM-and-GRU.pdf- 11-3-word-embeddings-and-translation.pdf
什么是PyTorch？（What is PyTorch?）- 入门
张量- 运算
NumPy桥- 将torch的Tensor转化为NumPy数组
将NumPy数组转化为Torch张量- CUDA上的张量
Autograd：自动求导- 张量
梯度- 神经网络（Neural Networks）
定义网络- 损失函数
反向传播- 更新权重
训练分类器（Training a Classifier）- 数据呢？
训练一个图片分类器- 1.加载并标准化CIFAR10
2.定义卷积神经网络- 3.定义损失函数和优化器
4.训练网络- 5.使用测试数据测试网络
在GPU上训练- 在多GPU上训练
接下来要做什么？- 选读：数据并行处理（Optional: Data Parallelism）
导入和参数- 虚拟数据集
简单模型- 创建一个模型和数据并行
运行模型- 结果
2个GPU- 3个GPU
8个GPU- 总结
张量(Tensors)- 热身：使用NumPy
PyTorch：张量(Tensors))- 自动求导(Autograd)
PyTorch：自动求导(Autograd))- PyTorch：定义自己的自动求导函数
TensorFlow：静态图nn模块(nnmodule)
PyTorch：神经网络模块nn- PyTorch：优化模块optim
PyTorch：定制神经网络nn模块- PyTorch：控制流和参数共享
Some code in this repo is separated in blocks using #%%.
A block is as same as a cell in Jupyter Notebook. So editors/IDEs supporting this functionality is recommanded.
Such as: