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requirements.txt 2.1 kB

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  1. #
  2. # pre-requirements (use python3.5 for better compability)
  3. # sudo apt-get install python3.5 python3.5-dev
  4. # sudo apt-get install python3-tk
  5. #
  6. #
  7. # pip
  8. # sudo apt-get install python-pip python3-pip
  9. # pip install pip -U
  10. # pip config set global.index-url 'https://mirrors.ustc.edu.cn/pypi/web/simple'
  11. #
  12. #
  13. # Install virtualenv
  14. # pip install setuptools
  15. # pip install virtualenv
  16. # pip install virtualenvwrapper
  17. # pip install virtualenvwrapper-win  #Windows使用该命令
  18. #
  19. # Add following lines to `~/.bashrc`
  20. # # virtualenv
  21. # export VIRTUALENVWRAPPER_PYTHON=/usr/bin/python
  22. # export WORKON_HOME=/home/bushuhui/virtualenv
  23. # source /usr/local/bin/virtualenvwrapper.sh 
  24. #
  25. # Usage:
  26. # # create virtual env
  27. # mkvirtualenv --python=/usr/local/python3.5.3/bin/python venv
  28. #
  29. # # active virtual env
  30. # workon venv
  31. #
  32. #
  33. # Install this list packages:
  34. # pip install -r requirements.txt
  35. #
  36. attrs==19.1.0
  37. backcall==0.1.0
  38. bleach==3.1.0
  39. certifi==2019.6.16
  40. chardet==3.0.4
  41. cycler==0.10.0
  42. decorator==4.4.0
  43. defusedxml==0.6.0
  44. entrypoints==0.3
  45. fire==0.2.1
  46. idna==2.8
  47. ipdb==0.12.2
  48. ipykernel==5.1.2
  49. ipython==7.8.0
  50. ipython-genutils==0.2.0
  51. ipywidgets==7.5.1
  52. jedi==0.15.1
  53. jieba==0.39
  54. Jinja2==2.10.1
  55. joblib==0.13.2
  56. jsonschema==3.0.2
  57. jupyter==1.0.0
  58. jupyter-client==5.3.1
  59. jupyter-console==6.0.0
  60. jupyter-core==4.5.0
  61. kiwisolver==1.1.0
  62. MarkupSafe==1.1.1
  63. matplotlib==3.0.3
  64. mistune==0.8.4
  65. nbconvert==5.6.0
  66. nbformat==4.4.0
  67. notebook==6.0.1
  68. numpy==1.17.1
  69. pandas==0.24.2
  70. pandocfilters==1.4.2
  71. parso==0.5.1
  72. patsy==0.5.1
  73. pexpect==4.7.0
  74. pickleshare==0.7.5
  75. Pillow==6.1.0
  76. prometheus-client==0.7.1
  77. prompt-toolkit==2.0.9
  78. ptyprocess==0.6.0
  79. Pygments==2.4.2
  80. pyparsing==2.4.2
  81. pyrsistent==0.15.4
  82. python-dateutil==2.8.0
  83. pytz==2019.2
  84. pyzmq==18.1.0
  85. qtconsole==4.5.5
  86. requests==2.22.0
  87. scikit-learn==0.21.3
  88. scipy==1.3.1
  89. Send2Trash==1.5.0
  90. six==1.12.0
  91. sklearn==0.0
  92. statsmodels==0.10.1
  93. termcolor==1.1.0
  94. terminado==0.8.2
  95. testpath==0.4.2
  96. torch==1.2.0
  97. torchfile==0.1.0
  98. torchvision==0.4.0
  99. tornado==6.0.3
  100. tqdm==4.35.0
  101. traitlets==4.3.2
  102. urllib3==1.25.3
  103. visdom==0.1.8.8
  104. wcwidth==0.1.7
  105. webencodings==0.5.1
  106. websocket-client==0.56.0
  107. widgetsnbextension==3.5.1

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》。