## Notebooks:

* machineLearning/10_digits_classification.ipynb
* MachineLearningNotebooks/05.%20Logistic%20Regression.ipynb
* MachineLearningNotebooks/08.%20Practical_NeuralNets.ipynb


## Exercise
* http://sofasofa.io/competitions.php?type=practice
* https://www.kaggle.com/competitions


* Titanic: notebooks/data-science-ipython-notebooks/kaggle/titanic.ipynb


## Method

* Programming Multiclass Logistic Regression
notebooks/MachineLearningNotebooks/05.%20Logistic%20Regression.ipynb

* Equation for MLP
notebooks/MachineLearningNotebooks/07.%20MLP%20Neural%20Networks.ipynb

* Optimization methods
notebooks/MachineLearningNotebooks/06.%20Optimization.ipynb


* https://github.com/wmpscc/DataMiningNotesAndPractice/blob/master/2.KMeans%E7%AE%97%E6%B3%95%E4%B8%8E%E4%BA%A4%E9%80%9A%E4%BA%8B%E6%95%85%E7%90%86%E8%B5%94%E5%AE%A1%E6%A0%B8%E9%A2%84%E6%B5%8B.md

* evaluation metrics
http://localhost:8889/notebooks/machineLearning/10_digits_classification.ipynb


* model selection and assessment
http://localhost:8889/notebooks/machineLearning/notebooks/01%20-%20Model%20Selection%20and%20Assessment.ipynb


## NN
* 神经网络——梯度下降&反向传播 https://blog.csdn.net/skullfang/article/details/78634317
* 零基础入门深度学习(3) - 神经网络和反向传播算法 https://www.zybuluo.com/hanbingtao/note/476663
* 如何直观地解释 backpropagation 算法？ https://www.zhihu.com/question/27239198
* 一文弄懂神经网络中的反向传播法——BackPropagation https://www.cnblogs.com/charlotte77/p/5629865.html

* https://medium.com/@UdacityINDIA/how-to-build-your-first-neural-network-with-python-6819c7f65dbf
* https://enlight.nyc/projects/neural-network/
* https://www.python-course.eu/neural_networks_with_python_numpy.php


## k-Means
* [如何使用 Keras 实现无监督聚类](http://m.sohu.com/a/236221126_717210)

## AutoEncoder (自编码/非监督学习)
* https://morvanzhou.github.io/tutorials/machine-learning/torch/4-04-autoencoder/
* https://github.com/MorvanZhou/PyTorch-Tutorial/blob/master/tutorial-contents/404_autoencoder.py
* pytorch AutoEncoder 自编码 https://www.jianshu.com/p/f0929f427d03
* Adversarial Autoencoders (with Pytorch) https://blog.paperspace.com/adversarial-autoencoders-with-pytorch/