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notebook_tips.py 1.8 kB

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  1. # ---
  2. # jupyter:
  3. # jupytext_format_version: '1.2'
  4. # kernelspec:
  5. # display_name: Python 3
  6. # language: python
  7. # name: python3
  8. # language_info:
  9. # codemirror_mode:
  10. # name: ipython
  11. # version: 3
  12. # file_extension: .py
  13. # mimetype: text/x-python
  14. # name: python
  15. # nbconvert_exporter: python
  16. # pygments_lexer: ipython3
  17. # version: 3.5.2
  18. # ---
  19. # ## Show LaTeX equation
  20. #
  21. #
  22. from IPython.display import Latex
  23. Latex(r"""\begin{eqnarray}
  24. \nabla \times \vec{\mathbf{B}} -\, \frac1c\, \frac{\partial\vec{\mathbf{E}}}{\partial t} & = \frac{4\pi}{c}\vec{\mathbf{j}} \\
  25. \nabla \cdot \vec{\mathbf{E}} & = 4 \pi \rho \\
  26. \nabla \times \vec{\mathbf{E}}\, +\, \frac1c\, \frac{\partial\vec{\mathbf{B}}}{\partial t} & = \vec{\mathbf{0}} \\
  27. \nabla \cdot \vec{\mathbf{B}} & = 0
  28. \end{eqnarray}""")
  29. # %%latex
  30. \begin{align}
  31. \nabla \times \vec{\mathbf{B}} -\, \frac1c\, \frac{\partial\vec{\mathbf{E}}}{\partial t} & = \frac{4\pi}{c}\vec{\mathbf{j}} \\
  32. \nabla \cdot \vec{\mathbf{E}} & = 4 \pi \rho \\
  33. \nabla \times \vec{\mathbf{E}}\, +\, \frac1c\, \frac{\partial\vec{\mathbf{B}}}{\partial t} & = \vec{\mathbf{0}} \\
  34. \nabla \cdot \vec{\mathbf{B}} & = 0
  35. \end{align}
  36. # ## Audio
  37. #
  38. from IPython.display import Audio
  39. Audio(url="http://www.nch.com.au/acm/8k16bitpcm.wav")
  40. # +
  41. import numpy as np
  42. max_time = 3
  43. f1 = 220.0
  44. f2 = 224.0
  45. rate = 8000.0
  46. L = 3
  47. times = np.linspace(0,L,rate*L)
  48. signal = np.sin(2*np.pi*f1*times) + np.sin(2*np.pi*f2*times)
  49. Audio(data=signal, rate=rate)
  50. # -
  51. # ## External sites
  52. # + {"scrolled": true}
  53. from IPython.display import IFrame
  54. IFrame('https://jupyter.org', width='100%', height=350)
  55. # -
  56. # ## References
  57. #
  58. # * https://nbviewer.jupyter.org/github/ipython/ipython/blob/master/examples/IPython%20Kernel/Index.ipynb

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》。