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References.md 2.4 kB

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  1. ## Notebooks:
  2. * machineLearning/10_digits_classification.ipynb
  3. * MachineLearningNotebooks/05.%20Logistic%20Regression.ipynb
  4. * MachineLearningNotebooks/08.%20Practical_NeuralNets.ipynb
  5. ## Exercise
  6. * http://sofasofa.io/competitions.php?type=practice
  7. * https://www.kaggle.com/competitions
  8. * Titanic: notebooks/data-science-ipython-notebooks/kaggle/titanic.ipynb
  9. * 使用神经网络解决拼图游戏 https://www.toutiao.com/a6855437347463365133/
  10. * [Sudoku-Solver](https://github.com/shivaverma/Sudoku-Solver)
  11. ## Method
  12. * Programming Multiclass Logistic Regression
  13. notebooks/MachineLearningNotebooks/05.%20Logistic%20Regression.ipynb
  14. * Equation for MLP
  15. notebooks/MachineLearningNotebooks/07.%20MLP%20Neural%20Networks.ipynb
  16. * Optimization methods
  17. notebooks/MachineLearningNotebooks/06.%20Optimization.ipynb
  18. * https://github.com/wmpscc/DataMiningNotesAndPractice/blob/master/2.KMeans%E7%AE%97%E6%B3%95%E4%B8%8E%E4%BA%A4%E9%80%9A%E4%BA%8B%E6%95%85%E7%90%86%E8%B5%94%E5%AE%A1%E6%A0%B8%E9%A2%84%E6%B5%8B.md
  19. * evaluation metrics
  20. http://localhost:8889/notebooks/machineLearning/10_digits_classification.ipynb
  21. * model selection and assessment
  22. http://localhost:8889/notebooks/machineLearning/notebooks/01%20-%20Model%20Selection%20and%20Assessment.ipynb
  23. ## NN
  24. * 神经网络——梯度下降&反向传播 https://blog.csdn.net/skullfang/article/details/78634317
  25. * 零基础入门深度学习(3) - 神经网络和反向传播算法 https://www.zybuluo.com/hanbingtao/note/476663
  26. * 如何直观地解释 backpropagation 算法? https://www.zhihu.com/question/27239198
  27. * 一文弄懂神经网络中的反向传播法——BackPropagation https://www.cnblogs.com/charlotte77/p/5629865.html
  28. * https://medium.com/@UdacityINDIA/how-to-build-your-first-neural-network-with-python-6819c7f65dbf
  29. * https://enlight.nyc/projects/neural-network/
  30. * https://www.python-course.eu/neural_networks_with_python_numpy.php
  31. ## k-Means
  32. * [如何使用 Keras 实现无监督聚类](http://m.sohu.com/a/236221126_717210)
  33. ## AutoEncoder (自编码/非监督学习)
  34. * https://morvanzhou.github.io/tutorials/machine-learning/torch/4-04-autoencoder/
  35. * https://github.com/MorvanZhou/PyTorch-Tutorial/blob/master/tutorial-contents/404_autoencoder.py
  36. * pytorch AutoEncoder 自编码 https://www.jianshu.com/p/f0929f427d03
  37. * Adversarial Autoencoders (with Pytorch) https://blog.paperspace.com/adversarial-autoencoders-with-pytorch/

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》。