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`‎README.md‎`

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`@@ -10,7 +10,6 @@`
`10`	`10`	`- [以下是一些独立的教程](#以下是一些独立的教程)`
`11`	`11`	`- [1) PyTorch深度学习:60分钟入门与实战](#1-pytorch深度学习60分钟入门与实战)`
`12`	`12`	`- [2) Learning PyTorch with Examples 用例子学习PyTorch](#2-learning-pytorch-with-examples-用例子学习pytorch)`
`13`		`- - [3) PyTorch1.0-Zero-To-All](#3-pytorch10-zero-to-all)`
`14`	`13`	`- [How to run? 推荐的运行方式](#how-to-run-推荐的运行方式)`
`15`	`14`
`16`	`15`	`## PyTorch 1.0 tutorials and examples`
`@@ -67,6 +66,10 @@`
`67`	`66`	`* 深度学习之Pytorch - 廖星宇.pdf`
`68`	`67`	`* 深度学习之PyTorch实战计算机视觉 - 唐进民.pdf`
`69`	`68`	`* [Udacity: Deep Learning with PyTorch](https://github.com/bat67/pytorch-tutorials-examples-and-books/tree/master/Udacity-Deep-Learning-with-PyTorch)`
	`69`	`+ <details>`
	`70`	`+ <summary>展开查看</summary>`
	`71`	`+ <pre>`
	`72`	`+ <code>`
`70`	`73`	`* Part 1: Introduction to PyTorch and using tensors`
`71`	`74`	`* Part 2: Building fully-connected neural networks with PyTorch`
`72`	`75`	`* Part 3: How to train a fully-connected network with backpropagation on MNIST`
`@@ -75,7 +78,34 @@`
`75`	`78`	`* Part 6: How to save and load trained models`
`76`	`79`	`* Part 7: Load image data with torchvision, also data augmentation`
`77`	`80`	`* Part 8: Use transfer learning to train a state-of-the-art image classifier for dogs and cats`
	`81`	`+ </code>`
	`82`	`+ </pre>`
	`83`	`+ </details>`
	`84`	`+* [PyTorch1.0-Zero-To-All](https://github.com/bat67/pytorch-tutorials-examples-and-books/tree/master/PyTorch-Zero-To-All-%5BPyTorch1.0%5D):Slides-newest from Google Drive`
	`85`	`+ <details>`
	`86`	`+ <summary>展开查看</summary>`
	`87`	`+ <pre>`
	`88`	`+ * Lecture 01_ Overview.pptx`
	`89`	`+ * Lecture 02_ Linear Model.pptx`
	`90`	`+ * Lecture 03_ Gradient Descent.pptx`
	`91`	`+ * Lecture 04_ Back-propagation and PyTorch autograd.pptx`
	`92`	`+ * Lecture 05_ Linear regression in PyTorch way.pptx`
	`93`	`+ * Lecture 06_ Logistic Regression.pptx`
	`94`	`+ * Lecture 07_ Wide _ Deep.pptx`
	`95`	`+ * Lecture 08_ DataLoader.pptx`
	`96`	`+ * Lecture 09_ Softmax Classifier.pptx`
	`97`	`+ * Lecture 10_ Basic CNN.pptx`
	`98`	`+ * Lecture 11_ Advanced CNN.pptx`
	`99`	`+ * Lecture 12_ RNN.pptx`
	`100`	`+ * Lecture 13_ RNN II.pptx`
	`101`	`+ * Lecture 14_ Seq2Seq.pptx`
	`102`	`+ * Lecture 15_ NSML, Smartest ML Platform.pptx`
	`103`	`+ </pre>`
	`104`	`+ </details>`
`78`	`105`	`* [Deep Learning Course Slides and Handout - fleuret.org](https://github.com/bat67/pytorch-tutorials-examples-and-books/tree/master/Deep-Learning-Course-Slides-and-Handout)`
	`106`	`+ <details>`
	`107`	`+ <summary>展开查看</summary>`
	`108`	`+ <pre>`
`79`	`109`	`* 1-1-from-anns-to-deep-learning.pdf`
`80`	`110`	`* 1-2-current-success.pdf`
`81`	`111`	`* 1-3-what-is-happening.pdf`
`@@ -132,7 +162,8 @@`
`132`	`162`	`* 11-1-RNN-basics.pdf`
`133`	`163`	`* 11-2-LSTM-and-GRU.pdf`
`134`	`164`	`* 11-3-word-embeddings-and-translation.pdf`
`135`		`-`
	`165`	`+ </pre>`
	`166`	`+ </details>`
`136`	`167`
`137`	`168`	`## 以下是一些独立的教程`
`138`	`169`
`@@ -210,30 +241,6 @@`
`210`	`241`
`211`	`242`
`212`	`243`
`213`		`-`
`214`		`-`
`215`		`-### 3) [PyTorch1.0-Zero-To-All](https://github.com/bat67/pytorch-tutorials-examples-and-books/tree/master/PyTorch-Zero-To-All-%5BPyTorch1.0%5D)`
`216`		`-`
`217`		`-* Slides-newest from Google Drive`
`218`		`- * Lecture 01_ Overview.pptx`
`219`		`- * Lecture 02_ Linear Model.pptx`
`220`		`- * Lecture 03_ Gradient Descent.pptx`
`221`		`- * Lecture 04_ Back-propagation and PyTorch autograd.pptx`
`222`		`- * Lecture 05_ Linear regression in PyTorch way.pptx`
`223`		`- * Lecture 06_ Logistic Regression.pptx`
`224`		`- * Lecture 07_ Wide _ Deep.pptx`
`225`		`- * Lecture 08_ DataLoader.pptx`
`226`		`- * Lecture 09_ Softmax Classifier.pptx`
`227`		`- * Lecture 10_ Basic CNN.pptx`
`228`		`- * Lecture 11_ Advanced CNN.pptx`
`229`		`- * Lecture 12_ RNN.pptx`
`230`		`- * Lecture 13_ RNN II.pptx`
`231`		`- * Lecture 14_ Seq2Seq.pptx`
`232`		`- * Lecture 15_ NSML, Smartest ML Platform.pptx`
`233`		`-`
`234`		`-`
`235`		`-`
`236`		`-`
`237`	`244`	`## How to run? 推荐的运行方式`
`238`	`245`
`239`	`246`	Some code in this repo is separated in blocks using `#%%`.
@@ -242,4 +249,7 @@ A block is as same as a cell in `Jupyter Notebook`. So editors/IDEs supporting t
`242`	`249`	`Such as:`
`243`	`250`	`* [VSCode](Functionality) with [Microsoft Python extension](https://marketplace.visualstudio.com/items?itemName=ms-python.python)`
`244`	`251`	`* [Spyder](https://pypi.org/project/spyder/) with [Anaconda](https://www.anaconda.com/)`
`245`		`-* [PyCharm](https://www.jetbrains.com/pycharm/)`
	`252`	`+* [PyCharm](https://www.jetbrains.com/pycharm/)`
	`253`	`+`
	`254`	`+`
	`255`	`+`

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