diff --git a/6_pytorch/2_CNN/2-batch-normalization.ipynb b/6_pytorch/2_CNN/2-batch-normalization.ipynb index f3a9d8b..7725e36 100644 --- a/6_pytorch/2_CNN/2-batch-normalization.ipynb +++ b/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, diff --git a/6_pytorch/2_CNN/3-lr-decay.ipynb b/6_pytorch/2_CNN/3-lr-decay.ipynb index 08ac346..7bb2c5d 100644 --- a/6_pytorch/2_CNN/3-lr-decay.ipynb +++ b/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, diff --git a/References.md b/References.md index 4dd3383..3add6b6 100644 --- a/References.md +++ b/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 diff --git a/demo_code/3_CNN_MNIST.py b/demo_code/3_CNN_MNIST.py index 4ea7df0..d7c0434 100644 --- a/demo_code/3_CNN_MNIST.py +++ b/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))