Move reference to code_cook

6_pytorch/2_CNN/2-batch-normalization.ipynb

@@ -561,7 +561,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.8"
+   "version": "3.5.2"
   }
  },
  "nbformat": 4,

6_pytorch/2_CNN/3-lr-decay.ipynb

@@ -403,7 +403,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.8"
+   "version": "3.5.2"
   }
  },
  "nbformat": 4,

References.md

@@ -1,79 +1,5 @@
 # References
 可以自行在下属列表找找到适合自己的学习资料，虽然罗列的比较多，但是个人最好选择一个深入阅读、练习。当练习到一定程度，可以再看看其他的资料，这样弥补单一学习资料可能存在的欠缺。
 
-
-
-## Notebook, Book, Tutorial
-
-* [Deep Learning with PyTorch](https://pytorch.org/deep-learning-with-pytorch-thank-you)
-* [Machine Learning Yearning 中文版 - 《机器学习训练秘籍》](https://github.com/deeplearning-ai/machine-learning-yearning-cn) ([在线阅读](https://deeplearning-ai.github.io/machine-learning-yearning-cn/))
-* [ipython-notebooks: A collection of IPython notebooks covering various topics](https://github.com/jdwittenauer/ipython-notebooks)
-* [Learn Data Science](http://learnds.com/)
-* [AM207 2016](https://github.com/AM207/2016/tree/master)
-* [Python机器学习](https://ljalphabeta.gitbooks.io/python-/content/)
-* [scientific-python-lectures](http://nbviewer.jupyter.org/github/jrjohansson/scientific-python-lectures/tree/master/)
-* [卷积神经网络中十大拍案叫绝的操作](https://www.toutiao.com/a6741309250070381070)
-
-
-
-## Python & IPython
-
-* [Python Numpy Tutorial - 简明Python, Numpy, Matplotlib教程](http://cs231n.github.io/python-numpy-tutorial/)
-* [Python教程](https://www.liaoxuefeng.com/wiki/0014316089557264a6b348958f449949df42a6d3a2e542c000)
-* [Python-Lectures](https://github.com/rajathkmp/Python-Lectures)
-* [A gallery of interesting Jupyter Notebooks](https://github.com/jupyter/jupyter/wiki/A-gallery-of-interesting-Jupyter-Notebooks)
-* [IPython tutorials](https://nbviewer.jupyter.org/github/ipython/ipython/blob/master/examples/IPython%20Kernel/Index.ipynb)
-* [Examples from the IPython mini-book](https://github.com/rossant/ipython-minibook)
-* [Code of the IPython Cookbook, Second Edition (2018)](https://github.com/ipython-books/cookbook-2nd-code)
-* [Essential Cheat Sheets for deep learning and machine learning researchers](https://github.com/kailashahirwar/cheatsheets-ai)
-* [手把手教你用Python做数据可视化](https://mp.weixin.qq.com/s/3Gwdjw8trwTR5uyr4G7EOg)
-
-
-
-## Libs
-
-* [numpy](http://www.numpy.org/)
-* [matplotlib - 2D and 3D plotting in Python](http://nbviewer.jupyter.org/github/jrjohansson/scientific-python-lectures/blob/master/Lecture-4-Matplotlib.ipynb)
-* [scipy](https://www.scipy.org/)
-* [pytorch](https://pytorch.org/)
-* [tensorflow](https://www.tensorflow.org/)
-* [keras](https://keras.io/)
-* [bokeh](https://bokeh.pydata.org/)
-
-
-
-
-## Awesome series & Collections
-
-* [Awesome Cmputer Vision](https://github.com/jbhuang0604/awesome-computer-vision)
-* [Awesome Deep Learning](https://github.com/ChristosChristofidis/awesome-deep-learning)
-* [Awesome - Most Cited Deep Learning Papers](https://github.com/terryum/awesome-deep-learning-papers)
-* [Awesome Deep Vision](https://github.com/kjw0612/awesome-deep-vision)
-* [Awesome 3D Reconstruction](https://github.com/openMVG/awesome_3DReconstruction_list)
-* [awesome-algorithm](https://github.com/apachecn/awesome-algorithm)
-* [Papers with code. Sorted by stars. Updated weekly.](https://github.com/zziz/pwc)
-
-
-
-## Lectures
-
-* [MIT 6.S094: Deep Learning for Self-Driving Cars](https://selfdrivingcars.mit.edu/)
-* [Deep Reinforcement Learning and Control](https://katefvision.github.io/)
-* [MIT Deep Learning](https://github.com/lexfridman/mit-deep-learning)
-* [Machine Learning](https://www.coursera.org/learn/machine-learning)
-* [CS229: Machine Learning](http://cs229.stanford.edu/)
-* [CS 20: Tensorflow for Deep Learning Research](http://web.stanford.edu/class/cs20si/index.html)
-* [CS 294: Deep Reinforcement Learning, UC Berkeley](http://rll.berkeley.edu/deeprlcourse/)
-* [Deep Learning Book](https://github.com/exacity/deeplearningbook-chinese)
-* [Machine Learning Crash Course with TensorFlow APIs](https://developers.google.cn/machine-learning/crash-course/)
-* [Nvidia DLI](https://www.nvidia.com/zh-cn/deep-learning-ai/education/)
-* [Introduction to Machine Learning](https://webdocs.cs.ualberta.ca/~nray1/CMPUT466_551.htm)
-* [Computer Vision @ ETHZ](http://cvg.ethz.ch/teaching/compvis/)
-* [SFMedu: A Structure from Motion System for Education](http://robots.princeton.edu/courses/SFMedu/)
-* [Scene understanding of computer vision](http://vision.princeton.edu/courses/COS598/2014sp/)
-* [Autonomous Navigation for Flying Robots](http://vision.in.tum.de/teaching/ss2015/autonavx)
-* [Multiple View Geometry](http://vision.in.tum.de/teaching/ss2015/mvg2015)
-* [Deep Learning for Self-Driving Cars](https://selfdrivingcars.mit.edu/)
-* [史上最全TensorFlow学习资源汇总](https://www.toutiao.com/a6543679835670053380/)
-* [Oxford Deep NLP 2017 course](https://github.com/oxford-cs-deepnlp-2017/lectures) 
+列表等在 https://gitee.com/pi-lab/code_cook/blob/master/doc/references/machine_learning/References.md
 

demo_code/3_CNN_MNIST.py

@@ -42,7 +42,7 @@ class Net_CNN(nn.Module):
 
     def forward(self, x):
         x = F.max_pool2d(F.relu(self.conv1(x)), (2, 2))
-        x = F.max_pool2d(F.relu(self.conv2(x)), 2)
+        x = F.max_pool2d(F.relu(self.conv2(x)), (2, 2))
         x = x.view(x.size()[0], -1)
         x = F.relu(self.fc1(x))
         x = F.relu(self.fc2(x))