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Add course introduction, some references, imporve circle dataset

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bushuhui 5 years ago
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"All the IPython Notebooks in this lecture series are available at https://github.com/rajathkumarmp/Python-Lectures"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Python-Lectures"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Introduction"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Python is a modern, robust, high level programming language. It is very easy to pick up even if you are completely new to programming."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Installation"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Mac OS X and Linux comes pre installed with python. Windows users can download python from https://www.python.org/downloads/ .\n",
"\n",
"To install IPython run,\n",
"\n",
" $ pip install ipython[all]\n",
" \n",
"This will install all the necessary dependencies for the notebook, qtconsole, tests etc."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Installation from unofficial distributions"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Installing all the necessary libraries might prove troublesome. Anaconda and Canopy comes pre packaged with all the necessary python libraries and also IPython."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Anaconda"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Download Anaconda from https://www.continuum.io/downloads\n",
"\n",
"Anaconda is completely free and includes more than 300 python packages. Both python 2.7 and 3.4 options are available."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Canopy"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Download Canopy from https://store.enthought.com/downloads/#default\n",
"\n",
"Canopy has a premium version which offers 300+ python packages. But the free version works just fine. Canopy as of now supports only 2.7 but it comes with its own text editor and IPython environment."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Launching IPython Notebook"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"From the terminal\n",
"\n",
" ipython notebook\n",
"\n",
"In Canopy and Anaconda, Open the respective terminals and execute the above."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## How to learn from this resource?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Download all the ipython notebooks from this repository https://github.com/rajathkumarmp/Python-Lectures\n",
"\n",
"Launch ipython notebook from the folder which contains the notebooks. Open each one of them\n",
"\n",
" Cell > All Output > Clear\n",
" \n",
"This will clear all the outputs and now you can understand each statement and learn interactively."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## License"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This work is licensed under the Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 1
}

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@@ -32,4 +32,4 @@ Python 是一门上手简单、功能强大、通用型的脚本编程语言。P
* [廖雪峰的Python教程](https://www.liaoxuefeng.com/wiki/1016959663602400)
* [智能系统实验室入门教程-Python](https://gitee.com/pi-lab/SummerCamp/tree/master/python)
* [Python tips](../tips/python)
* [Get Started with Python](Python.pdf)

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},
{
"cell_type": "code",
"execution_count": 2,
"execution_count": 1,
"metadata": {},
"outputs": [
{
@@ -180,7 +180,7 @@
},
{
"cell_type": "code",
"execution_count": 1,
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
@@ -196,7 +196,7 @@
},
{
"cell_type": "code",
"execution_count": 2,
"execution_count": 3,
"metadata": {},
"outputs": [
{
@@ -222,7 +222,7 @@
"Text(0.5, 1.0, 'Original Data')"
]
},
"execution_count": 2,
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
},
@@ -252,7 +252,7 @@
},
{
"cell_type": "code",
"execution_count": 3,
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
@@ -280,7 +280,7 @@
},
{
"cell_type": "code",
"execution_count": 4,
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
@@ -324,7 +324,7 @@
},
{
"cell_type": "code",
"execution_count": 5,
"execution_count": 6,
"metadata": {},
"outputs": [
{
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},
{
"cell_type": "code",
"execution_count": 6,
"execution_count": 7,
"metadata": {},
"outputs": [
{


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@@ -18,9 +18,9 @@
"\n",
"心理学家Rosenblatt构想了感知机，它作为简化的数学模型解释大脑神经元如何工作：它取一组二进制输入值（附近的神经元），将每个输入值乘以一个连续值权重（每个附近神经元的突触强度），并设立一个阈值，如果这些加权输入值的和超过这个阈值，就输出1，否则输出0（同理于神经元是否放电）。对于感知机，绝大多数输入值不是一些数据，就是别的感知机的输出值。\n",
"\n",
"麦卡洛克-皮兹模型缺乏一个对AI而言至关重要的学习机制。这就是感知机更出色的地方所在——罗森布拉特受到唐纳德·赫布(Donald Hebb) 基础性工作的启发，想出一个让这种人工神经元学习的办法。赫布提出了一个出人意料并影响深远的想法，称知识和学习发生在大脑主要是通过神经元间突触的形成与变化，简要表述为赫布法则：\n",
"唐纳德·赫布提出了一个出人意料并影响深远的想法，称知识和学习发生在大脑主要是通过神经元间突触的形成与变化，简要表述为赫布法则：\n",
"\n",
">当细胞A的轴突足以接近以激发细胞B，并反复持续地对细胞B放电，一些生长过程或代谢变化将发生在某一个或这两个细胞内，以致A作为对B放电的细胞中的一个，效率增加。\n",
"> 当细胞A的轴突足以接近以激发细胞B，并反复持续地对细胞B放电，一些生长过程或代谢变化将发生在某一个或这两个细胞内，以致A作为对B放电的细胞中的一个，效率增加。\n",
"\n",
"\n",
"感知机并没有完全遵循这个想法，**但通过调输入值的权重，可以有一个非常简单直观的学习方案：给定一个有输入输出实例的训练集，感知机应该「学习」一个函数：对每个例子，若感知机的输出值比实例低太多，则增加它的权重，否则若设比实例高太多，则减少它的权重。**\n"
@@ -32,13 +32,13 @@
"source": [
"## 1. 感知机模型\n",
"\n",
"假设输入空间(特征向量)为X⊆Rn，输出空间为Y={-1, +1}。输入x∈X表示实例的特征向量，对应于输入空间的点；输出y∈Y表示示例的类别。由输入空间到输出空间的函数为\n",
"假设输入空间(特征向量)为$X \\subseteq R^n$，输出空间为$Y=\\{-1, +1\\}$。输入$x \\in X$ 表示实例的特征向量，对应于输入空间的点；输出$y \\in Y$表示示例的类别。由输入空间到输出空间的函数为\n",
"\n",
"$$\n",
"f(x) = sign(w x + b)\n",
"$$\n",
"\n",
"称为感知机。其中，参数w叫做权值向量，b称为偏置。w·x表示w和x的内积。sign为符号函数，即\n",
"称为感知机。其中，参数$w$叫做权值向量，$b$称为偏置。$w·x$表示$w$和$x$的内积。$sign$为符号函数，即\n",
"![sign_function](images/sign.png)\n",
"\n",
"### 1.1 几何解释 \n",
@@ -66,7 +66,7 @@
"\\frac{1}{||w||} | w \\cdot xo + b |\n",
"$$\n",
"\n",
"其次，对于误分类点（xi,yi）来说 -yi(w·xi+b)>0\n",
"其次，对于误分类点$(x_i,y_i)$来说 $-y_i(w \\cdot x_i + b) > 0$\n",
"\n",
"这样，假设超平面S的总的误分类点集合为M，那么所有误分类点到S的距离之和为\n",
"$$\n",
@@ -76,7 +76,7 @@
"\n",
"### 经验风险函数\n",
"\n",
"给定数据集T={(x1,y1),(x2,y2)...(xN,yN)}（其中xi∈X=Rn，yi∈Y={-1, +1}，i=1,2...N），感知机sign(w·x+b)学习的损失函数定义为\n",
"给定数据集$T = \\{(x_1,y_1), (x_2, y_2), ... (x_N, y_N)\\}$（其中$x_i \\in R^n$, $y_i \\in \\{-1, +1\\}，i=1,2...N$），感知机sign(w·x+b)学习的损失函数定义为\n",
"$$\n",
"L(w, b) = - \\sum_{x_i \\in M} y_i (w \\cdot x_i + b)\n",
"$$\n",
@@ -92,13 +92,13 @@
"## 3. 感知机学习算法\n",
"\n",
"\n",
"最优化问题：给定数据集T={(x1,y1),(x2,y2)...(xN,yN)}（其中xi∈X=Rn，yi∈Y={-1, +1}，i=1,2...N），求参数w,b,使其成为损失函数的解（M为误分类的集合）：\n",
"最优化问题：给定数据集$T = \\{(x_1,y_1), (x_2, y_2), ... (x_N, y_N)\\}$（其中$x_i \\in R^n$, $y_i \\in \\{-1, +1\\}，i=1,2...N$），求参数w,b,使其成为损失函数的解（M为误分类的集合）：\n",
"\n",
"$$\n",
"min_{w,b} L(w, b) = - \\sum_{x_i \\in M} y_i (w \\cdot x_i + b)\n",
"$$\n",
"\n",
"感知机学习是误分类驱动的，具体采用[随机梯度下降法](https://blog.csdn.net/zbc1090549839/article/details/38149561)。首先，任意选定$w_0$、$b_0$，然后用梯度下降法不断极小化目标函数，极小化的过程不知一次性的把M中的所有误分类点梯度下降，而是一次随机选取一个误分类点使其梯度下降。\n",
"感知机学习是误分类驱动的，具体采用[随机梯度下降法](https://blog.csdn.net/zbc1090549839/article/details/38149561)。首先，任意选定$w_0$、$b_0$，然后用梯度下降法不断极小化目标函数，极小化的过程不是一次性的把M中的所有误分类点梯度下降，而是一次随机选取一个误分类点使其梯度下降。\n",
"\n",
"假设误分类集合M是固定的，那么损失函数L(w,b)的梯度为\n",
"$$\n",
@@ -150,10 +150,11 @@
"name": "stdout",
"output_type": "stream",
"text": [
"update weight and bias: 1.0 2.5 0.5\n",
"update weight and bias: -2.0 1.5 0.0\n",
"w = [-2.0, 1.5]\n",
"b = 0.0\n",
"update weight and bias: 1.0 3.0 0.5\n",
"update weight and bias: -0.5 2.5 0.0\n",
"update weight and bias: -2.5 2.0 -0.5\n",
"w = [-2.5, 2.0]\n",
"b = -0.5\n",
"[ 1 1 1 1 -1 -1 -1 -1]\n",
"[1, 1, 1, 1, -1, -1, -1, -1]\n"
]


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README.md View File

@@ -1,13 +1,14 @@
# 机器学习

本教程主要讲解机器学习的基本原理与实现，通过本教程的引导来快速学习Python、Python的常用库、机器学习的理论知识与实际编程，并学习如何解决实际问题。
本教程主要讲解机器学习的基本原理与实现，通过本教程的引导来快速学习Python、Python常用库、机器学习的理论知识与实际编程，并学习如何解决实际问题。

由于**本课程需要大量的编程练习才能取得比较好的学习效果**，因此需要认真去做[作业和报告](https://gitee.com/pi-lab/machinelearning_homework)，写作业的过程可以查阅网上的资料，但是不能直接照抄，需要自己独立思考并独立写出代码。
由于**本课程需要大量的编程练习才能取得比较好的学习效果**，因此需要认真去完成[作业和报告](https://gitee.com/pi-lab/machinelearning_homework)，写作业的过程可以查阅网上的资料，但是不能直接照抄，需要自己独立思考并独立写出代码。



## 1. 内容
1. [Python](0_python/)
1. [课程简介](CourseIntroduction.pdf)
2. [Python](0_python/)
- [Install Python](tips/InstallPython.md)
- [Introduction](0_python/0_Introduction.ipynb)
- [Python Basics](0_python/1_Basics.ipynb)
@@ -17,20 +18,20 @@
- [Control Flow](0_python/5_Control_Flow.ipynb)
- [Function](0_python/6_Function.ipynb)
- [Class](0_python/7_Class.ipynb)
2. [numpy & matplotlib](1_numpy_matplotlib_scipy_sympy/)
3. [numpy & matplotlib](1_numpy_matplotlib_scipy_sympy/)
- [numpy](1_numpy_matplotlib_scipy_sympy/numpy_tutorial.ipynb)
- [matplotlib](1_numpy_matplotlib_scipy_sympy/matplotlib_simple_tutorial.ipynb)
- [ipython & notebook](1_numpy_matplotlib_scipy_sympy/ipython_notebook.ipynb)
3. [knn](2_knn/knn_classification.ipynb)
4. [kMenas](3_kmeans/k-means.ipynb)
5. [Logistic Regression](4_logistic_regression/)
4. [knn](2_knn/knn_classification.ipynb)
5. [kMenas](3_kmeans/k-means.ipynb)
6. [Logistic Regression](4_logistic_regression/)
- [Least squares](4_logistic_regression/Least_squares.ipynb)
- [Logistic regression](4_logistic_regression/Logistic_regression.ipynb)
6. [Neural Network](5_nn/)
7. [Neural Network](5_nn/)
- [Perceptron](5_nn/Perceptron.ipynb)
- [Multi-layer Perceptron & BP](5_nn/mlp_bp.ipynb)
- [Softmax & cross-entroy](5_nn/softmax_ce.ipynb)
7. [PyTorch](6_pytorch/)
8. [PyTorch](6_pytorch/)
- Basic
- [short tutorial](6_pytorch/PyTorch_quick_intro.ipynb)
- [basic/Tensor-and-Variable](6_pytorch/0_basic/Tensor-and-Variable.ipynb)
@@ -67,18 +68,18 @@



## 2. 其他参考
## 2. 其他参考资料
* 资料速查
* [相关学习参考资料等](References.md)
* [相关学习参考资料汇总](References.md)
* [一些速查手册](tips/cheatsheet)

* 机器学习方面
* 机器学习方面技巧等
* [Confusion Matrix](tips/confusion_matrix.ipynb)
* [Datasets](tips/datasets.ipynb)
* [构建深度神经网络的一些实战建议](tips/构建深度神经网络的一些实战建议.md)
* [Intro to Deep Learning](tips/Intro_to_Deep_Learning.pdf)

* Python
* Python技巧等
* [安装Python环境](tips/InstallPython.md)
* [Python tips](tips/python)

@@ -90,4 +91,3 @@
* Markdown
* [Markdown——入门指南](https://www.jianshu.com/p/1e402922ee32)



+ 3
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@@ -5,6 +5,7 @@

## 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)
@@ -28,8 +29,9 @@



## Machine learning
## Notebook, Book, Tutorial

* [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)


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@@ -1,4 +1,4 @@
# Installing Python environments
# Installing Python Environments

由于Python的库比较多，并且依赖关系比较复杂，所以请仔细阅读下面的说明，使用下面的说明来安装能够减少问题的可能。*不过所列的安装方法，里面存在较多的细节，也许和你的系统并不适配，所以会遇到问题。如果遇到问题请通过搜索引擎去查找解决的办法*，通过这个方式锻炼自己解决问题的能力。



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@@ -1,400 +1,400 @@
9.862597006109558251e+00,4.388945920860357397e+00,0.000000000000000000e+00
2.877170945049199346e+00,1.202827750793628248e+01,0.000000000000000000e+00
3.337256239136289437e+00,-5.220370923066885105e+00,0.000000000000000000e+00
6.493095562032711321e+00,-1.113144595320488328e+01,0.000000000000000000e+00
9.160816030038004598e+00,-1.009779300537452151e+01,0.000000000000000000e+00
1.041802753742201126e+01,1.894574992636611332e+00,0.000000000000000000e+00
-9.011386818141570387e+00,7.113786261861553051e+00,0.000000000000000000e+00
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+ 6
- 4
tips/datasets.ipynb
File diff suppressed because it is too large
View File


+ 4
- 4
tips/notebook_tips.ipynb View File

@@ -10,7 +10,7 @@
},
{
"cell_type": "code",
"execution_count": 7,
"execution_count": 1,
"metadata": {},
"outputs": [
{
@@ -48,7 +48,7 @@
"<IPython.core.display.HTML object>"
]
},
"execution_count": 7,
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
@@ -87,7 +87,7 @@
},
{
"cell_type": "code",
"execution_count": 6,
"execution_count": 2,
"metadata": {},
"outputs": [
{
@@ -104,7 +104,7 @@
"<IPython.core.display.Latex object>"
]
},
"execution_count": 6,
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}


+ 0
- 55
tips/requirements.txt View File

@@ -1,55 +0,0 @@
#
# pre-requirements (use python3.5 for better compability)
# sudo apt-get install python3.5 python3.5-dev
# sudo apt-get install python3-tk
#

#
# pip
# sudo apt-get install python-pip python3-pip
# pip install pip -U
# pip config set global.index-url 'https://mirrors.ustc.edu.cn/pypi/web/simple'
#

#
# Install virtualenv
# pip install setuptools
# pip install virtualenv
# pip install virtualenvwrapper
# pip install virtualenvwrapper-win　　#Windows使用该命令
#
# Add following lines to `~/.bashrc`
# # virtualenv
# export VIRTUALENVWRAPPER_PYTHON=/usr/bin/python
# export WORKON_HOME=/home/bushuhui/virtualenv
# source /usr/local/bin/virtualenvwrapper.sh　
#
# Usage:
# # create virtual env
# mkvirtualenv --python=/usr/local/python3.5.3/bin/python venv
#
# # active virtual env
# workon venv
#

#
# Install this list packages:
# pip install -r requirements.txt
#


numpy
matplotlib==3.0.3
sklearn
statsmodels
pandas==0.24.2
ipython
jupyter

fire
torchvision
visdom
jieba
ipdb
tqdm


+ 5
- 0
tips/构建深度神经网络的一些实战建议.md View File

@@ -6,6 +6,11 @@



更多详细的深度学习技巧等可以参考：
* [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/))



## 常见的一些tips

**1、使用Adam作为优化器。** Adam优化效果非常好。与传统的优化器（optimizer），如传统的梯度下降法，它应该是首选。Tensorflow实践笔记：当保存和回复模型参数时，设置AdamOptimizer之后，一定记得设置Saver，因为Adam有些state也需要恢复（即每个weight的学习率）。


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