You can not select more than 25 topics Topics must start with a chinese character,a letter or number, can include dashes ('-') and can be up to 35 characters long.

README.md 6.1 kB

6 years ago
6 years ago
6 years ago
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899100101102103104105106107108109110111
  1. # 机器学习
  2. 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。
  3. 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。
  4. ![Machine Learning Cover](images/machine_learning.png)
  5. ## 1. 内容
  6. 1. [课程简介](CourseIntroduction.pdf)
  7. 2. [Python](0_python/)
  8. - [Install Python](tips/InstallPython.md)
  9. - [Introduction](0_python/0_Introduction.ipynb)
  10. - [Python Basics](0_python/1_Basics.ipynb)
  11. - [Print Statement](0_python/2_Print_Statement.ipynb)
  12. - [Data Structure 1](0_python/3_Data_Structure_1.ipynb)
  13. - [Data Structure 2](0_python/4_Data_Structure_2.ipynb)
  14. - [Control Flow](0_python/5_Control_Flow.ipynb)
  15. - [Function](0_python/6_Function.ipynb)
  16. - [Class](0_python/7_Class.ipynb)
  17. 3. [numpy & matplotlib](1_numpy_matplotlib_scipy_sympy/)
  18. - [numpy](1_numpy_matplotlib_scipy_sympy/numpy_tutorial.ipynb)
  19. - [matplotlib](1_numpy_matplotlib_scipy_sympy/matplotlib_simple_tutorial.ipynb)
  20. - [ipython & notebook](1_numpy_matplotlib_scipy_sympy/ipython_notebook.ipynb)
  21. 4. [knn](2_knn/knn_classification.ipynb)
  22. 5. [kMenas](3_kmeans/k-means.ipynb)
  23. 6. [Logistic Regression](4_logistic_regression/)
  24. - [Least squares](4_logistic_regression/Least_squares.ipynb)
  25. - [Logistic regression](4_logistic_regression/Logistic_regression.ipynb)
  26. 7. [Neural Network](5_nn/)
  27. - [Perceptron](5_nn/Perceptron.ipynb)
  28. - [Multi-layer Perceptron & BP](5_nn/mlp_bp.ipynb)
  29. - [Softmax & cross-entroy](5_nn/softmax_ce.ipynb)
  30. 8. [PyTorch](6_pytorch/)
  31. - Basic
  32. - [short tutorial](6_pytorch/PyTorch_quick_intro.ipynb)
  33. - [basic/Tensor-and-Variable](6_pytorch/0_basic/Tensor-and-Variable.ipynb)
  34. - [basic/autograd](6_pytorch/0_basic/autograd.ipynb)
  35. - [basic/dynamic-graph](6_pytorch/0_basic/dynamic-graph.ipynb)
  36. - NN & Optimization
  37. - [nn/linear-regression-gradient-descend](6_pytorch/1_NN/linear-regression-gradient-descend.ipynb)
  38. - [nn/logistic-regression](6_pytorch/1_NN/logistic-regression.ipynb)
  39. - [nn/nn-sequential-module](6_pytorch/1_NN/nn-sequential-module.ipynb)
  40. - [nn/bp](6_pytorch/1_NN/bp.ipynb)
  41. - [nn/deep-nn](6_pytorch/1_NN/deep-nn.ipynb)
  42. - [nn/param_initialize](6_pytorch/1_NN/param_initialize.ipynb)
  43. - [optim/sgd](6_pytorch/1_NN/optimizer/sgd.ipynb)
  44. - [optim/adam](6_pytorch/1_NN/optimizer/adam.ipynb)
  45. - CNN
  46. - [CNN simple demo](demo_code/3_CNN_MNIST.py)
  47. - [cnn/basic_conv](6_pytorch/2_CNN/basic_conv.ipynb)
  48. - [cnn/minist (demo code)](./demo_code/3_CNN_MNIST.py)
  49. - [cnn/batch-normalization](6_pytorch/2_CNN/batch-normalization.ipynb)
  50. - [cnn/regularization](6_pytorch/2_CNN/regularization.ipynb)
  51. - [cnn/lr-decay](6_pytorch/2_CNN/lr-decay.ipynb)
  52. - [cnn/vgg](6_pytorch/2_CNN/vgg.ipynb)
  53. - [cnn/googlenet](6_pytorch/2_CNN/googlenet.ipynb)
  54. - [cnn/resnet](6_pytorch/2_CNN/resnet.ipynb)
  55. - [cnn/densenet](6_pytorch/2_CNN/densenet.ipynb)
  56. - RNN
  57. - [rnn/pytorch-rnn](6_pytorch/3_RNN/pytorch-rnn.ipynb)
  58. - [rnn/rnn-for-image](6_pytorch/3_RNN/rnn-for-image.ipynb)
  59. - [rnn/lstm-time-series](6_pytorch/3_RNN/time-series/lstm-time-series.ipynb)
  60. - GAN
  61. - [gan/autoencoder](6_pytorch/4_GAN/autoencoder.ipynb)
  62. - [gan/vae](6_pytorch/4_GAN/vae.ipynb)
  63. - [gan/gan](6_pytorch/4_GAN/gan.ipynb)
  64. ## 2. 学习的建议
  65. 1. 为了更好的学习本课程,需要大家把Python编程的基础能力培养好,这样后续的机器学习方法学习才比较扎实。
  66. 2. 每个课程前部分是理论基础,然后是代码实现。个人如果想学的更扎实,可以自己把各个方法的代码亲自实现一下。做的过程尽可能自己想解决办法,因为重要的学习目标不是代码本身,而是学会分析问题、解决问题的能力。
  67. ## 3. 其他参考资料
  68. * 资料速查
  69. * [相关学习参考资料汇总](References.md)
  70. * [一些速查手册](tips/cheatsheet)
  71. * 机器学习方面技巧等
  72. * [Confusion Matrix](tips/confusion_matrix.ipynb)
  73. * [Datasets](tips/datasets.ipynb)
  74. * [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)
  75. * [Intro to Deep Learning](tips/Intro_to_Deep_Learning.pdf)
  76. * Python技巧等
  77. * [安装Python环境](tips/InstallPython.md)
  78. * [Python tips](tips/python)
  79. * Git
  80. * [Git Tips - 常用方法速查,快速入门](https://gitee.com/pi-lab/learn_programming/blob/master/6_tools/git/git-tips.md)
  81. * [Git快速入门 - Git初体验](https://my.oschina.net/dxqr/blog/134811)
  82. * [在win7系统下使用TortoiseGit(乌龟git)简单操作Git](https://my.oschina.net/longxuu/blog/141699)
  83. * [Git系统学习 - 廖雪峰的Git教程](https://www.liaoxuefeng.com/wiki/0013739516305929606dd18361248578c67b8067c8c017b000)
  84. * Markdown
  85. * [Markdown——入门指南](https://www.jianshu.com/p/1e402922ee32)
  86. ## 4. 相关学习资料参考
  87. 在上述内容学习完成之后,可以进行更进一步机器学习、计算机视觉方面的学习与研究,具体的资料可以参考:
  88. 1. [《一步一步学编程》](https://gitee.com/pi-lab/learn_programming)
  89. 2. 智能系统实验室-培训教程与作业
  90. - [《智能系统实验室-暑期培训教程》](https://gitee.com/pi-lab/SummerCamp)
  91. - [《智能系统实验室-暑期培训作业》](https://gitee.com/pi-lab/SummerCampHomework)
  92. 3. [智能系统实验室研究课题](https://gitee.com/pi-lab/pilab_research_fields3. [智能系统实验室研究课题](https://gitee.com/pi-lab/pilab_research_fields)
  93. 4. [编程代码参考、技巧集合](https://gitee.com/pi-lab/code_cook)
  94. - 可以在这个代码、技巧集合中找到某项功能的示例,从而加快自己代码的编写

机器学习越来越多应用到飞行器、机器人等领域,其目的是利用计算机实现类似人类的智能,从而实现装备的智能化与无人化。本课程旨在引导学生掌握机器学习的基本知识、典型方法与技术,通过具体的应用案例激发学生对该学科的兴趣,鼓励学生能够从人工智能的角度来分析、解决飞行器、机器人所面临的问题和挑战。本课程主要内容包括Python编程基础,机器学习模型,无监督学习、监督学习、深度学习基础知识与实现,并学习如何利用机器学习解决实际问题,从而全面提升自我的《综合能力》。