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requirements.txt 2.3 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.aliyun.com/pypi/simple/'
  11. #
  12. # or write following to '~/.config/pip/pip.conf'
  13. # [global]
  14. # timeout = 6000
  15. # index-url = https://mirrors.aliyun.com/pypi/simple/
  16. #
  17. # Install virtualenv by pip
  18. # pip install setuptools
  19. # pip install virtualenv
  20. # pip install virtualenvwrapper
  21. # pip install virtualenvwrapper-win  #Windows使用该命令
  22. #
  23. # Add following lines to `~/.bashrc`
  24. # # virtualenv
  25. # export VIRTUALENVWRAPPER_PYTHON=/usr/bin/python3
  26. # export WORKON_HOME=/home/bushuhui/virtualenv
  27. # source /usr/local/bin/virtualenvwrapper.sh 
  28. #
  29. #
  30. # Usage:
  31. # # create virtual env
  32. # mkvirtualenv --python=/usr/local/python3.5.3/bin/python venv
  33. #
  34. # # active virtual env
  35. # workon venv
  36. #
  37. #
  38. # Install this list packages:
  39. # pip install -r requirements.txt
  40. # Save package list
  41. # pip freeze >> requirements.txt
  42. attrs==19.1.0
  43. backcall==0.1.0
  44. bleach==3.1.0
  45. certifi==2019.6.16
  46. chardet==3.0.4
  47. cycler==0.10.0
  48. decorator==4.4.0
  49. defusedxml==0.6.0
  50. entrypoints==0.3
  51. fire==0.2.1
  52. idna==2.8
  53. ipdb==0.12.2
  54. ipykernel==5.1.2
  55. ipython==7.8.0
  56. ipython-genutils==0.2.0
  57. ipywidgets==7.5.1
  58. jedi==0.15.1
  59. jieba==0.39
  60. Jinja2==2.10.1
  61. joblib==0.13.2
  62. jsonschema==3.0.2
  63. jupyter==1.0.0
  64. jupyter-client==5.3.1
  65. jupyter-console==6.0.0
  66. jupyter-core==4.5.0
  67. kiwisolver==1.1.0
  68. MarkupSafe==1.1.1
  69. matplotlib==3.0.3
  70. mistune==0.8.4
  71. nbconvert==5.6.0
  72. nbformat==4.4.0
  73. notebook==6.0.1
  74. numpy==1.17.1
  75. pandas==0.24.2
  76. pandocfilters==1.4.2
  77. parso==0.5.1
  78. patsy==0.5.1
  79. pexpect==4.7.0
  80. pickleshare==0.7.5
  81. Pillow==6.1.0
  82. prometheus-client==0.7.1
  83. prompt-toolkit==2.0.9
  84. ptyprocess==0.6.0
  85. Pygments==2.4.2
  86. pyparsing==2.4.2
  87. pyrsistent==0.15.4
  88. python-dateutil==2.8.0
  89. pytz==2019.2
  90. pyzmq==18.1.0
  91. qtconsole==4.5.5
  92. requests==2.22.0
  93. scikit-learn==0.21.3
  94. scipy==1.3.1
  95. Send2Trash==1.5.0
  96. six==1.12.0
  97. sklearn==0.0
  98. statsmodels==0.10.1
  99. termcolor==1.1.0
  100. terminado==0.8.2
  101. testpath==0.4.2
  102. torch==1.2.0
  103. torchfile==0.1.0
  104. torchvision==0.4.0
  105. tornado==6.0.3
  106. tqdm==4.35.0
  107. traitlets==4.3.2
  108. urllib3==1.25.3
  109. visdom==0.1.8.8
  110. wcwidth==0.1.7
  111. webencodings==0.5.1
  112. websocket-client==0.56.0
  113. 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》。