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bushuhui
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4 years ago | |
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| cheatsheet | 4 years ago | |
| images | 5 years ago | |
| logistic_regression_demo | 5 years ago | |
| nn | 5 years ago | |
| python | 5 years ago | |
| pytorch | 5 years ago | |
| supervised_learning | 5 years ago | |
| InstallPython.md | 4 years ago | |
| InstallPython_EN.md | 5 years ago | |
| Intro_to_Deep_Learning.pdf | 5 years ago | |
| Matplotlib.ipynb | 5 years ago | |
| SciPy.ipynb | 5 years ago | |
| Scikit-learn.ipynb | 5 years ago | |
| Seaborn.ipynb | 5 years ago | |
| Statsmodels.ipynb | 5 years ago | |
| The Matrix Calculus You Need For Deep Learning.pdf | 5 years ago | |
| confusion_matrix.ipynb | 5 years ago | |
| dataset_CIFAR-10.py | 5 years ago | |
| dataset_circles.csv | 5 years ago | |
| datasets.ipynb | 4 years ago | |
| notebook_tips.ipynb | 5 years ago | |
| 构建深度神经网络的一些实战建议.md | 5 years ago | |
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》。
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