bushuhui
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| ## 3. 参考资料 | |||
| ## 3. [参考资料](References.md) | |||
| * [教程、代码](References.md) | |||
| * 资料速查 | |||
| * [相关学习参考资料汇总](References.md) | |||
| * [一些速查手册](references_tips/cheatsheet) | |||
| * 机器学习方面技巧等 | |||
| * [Confusion Matrix](references_tips/confusion_matrix.ipynb) | |||
| * [Datasets](references_tips/datasets.ipynb) | |||
| * [构建深度神经网络的一些实战建议](references_tips/构建深度神经网络的一些实战建议.md) | |||
| * [Intro to Deep Learning](references_tips/Intro_to_Deep_Learning.pdf) | |||
| * Python技巧等 | |||
| * [安装Python环境](references_tips/InstallPython.md) | |||
| * [Python tips](references_tips/python) | |||
| * [Git教程](https://gitee.com/pi-lab/learn_programming/blob/master/6_tools/git/README.md) | |||
| * [Markdown教程](https://gitee.com/pi-lab/learn_programming/blob/master/6_tools/markdown/README.md) | |||
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| # References | |||
| # 参考资料 | |||
| 可以自行在下属列表找找到适合自己的学习资料，虽然罗列的比较多，但是个人最好选择一个深入阅读、练习。当练习到一定程度，可以再看看其他的资料，这样弥补单一学习资料可能存在的欠缺。 | |||
| 列表等在 https://gitee.com/pi-lab/pilab_research_fields/blob/master/references/ML_References.md | |||
| ## 1. 教程、代码 | |||
| ## References | |||
| ### 1.1 教程 | |||
| * [《动手学深度学习》 — 动手学深度学习 2.0.0-alpha2 documentation](https://zh-v2.d2l.ai/index.html) | |||
| * [Introduction — Neuromatch Academy: Deep Learning](https://deeplearning.neuromatch.io/tutorials/intro.html) | |||
| ### 1.2 代码 | |||
| * [《统计学习方法》的代码](https://gitee.com/afishoutis/MachineLearning) | |||
| * [《统计学习方法》pytorch实现](https://github.com/fengdu78/lihang-code) | |||
| * [pytorch-cifar100](https://github.com/weiaicunzai/pytorch-cifar100) 实现ResNet, DenseNet, VGG, GoogleNet, InceptionV3, InceptionV4, Inception-ResNetv2, Xception, Resnet In Resnet, ResNext,ShuffleNet, ShuffleNetv2, MobileNet, MobileNetv2, SqueezeNet, NasNet, Residual Attention Network, SENet, WideResNet | |||
| * [Attention: xmu-xiaoma666/External-Attention-pytorch: Pytorch implementation of various Attention Mechanisms, MLP, Re-parameter, Convolution, which is helpful to further understand papers.⭐⭐⭐ (github.com)](https://github.com/xmu-xiaoma666/External-Attention-pytorch) 注意力机制，多层神经网络，重参数。 | |||
| * [Python TheAlgorithms/Python: All Algorithms implemented in Python (github.com)](https://github.com/TheAlgorithms/Python) | |||
| * PytTorch 训练手册 https://github.com/zergtant/pytorch-handbook | |||
| ## 2. 工具、技巧 | |||
| * [形象直观了解谷歌大脑新型优化器LAMB](https://www.toutiao.com/i6687162064395305475/) | |||
| * [梯度下降方法的视觉解释（动量，AdaGrad，RMSProp，Adam）](https://www.toutiao.com/i6836422484028293640/) | |||
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| ## Course & Code | |||
| - Course | |||
| - [《动手学深度学习》 — 动手学深度学习 2.0.0-alpha2 documentation](https://zh-v2.d2l.ai/index.html) | |||
| - [连接高校和企业 - 蓝桥云课](https://www.lanqiao.cn/) | |||
| - [Introduction — Neuromatch Academy: Deep Learning](https://deeplearning.neuromatch.io/tutorials/intro.html) | |||
| - code | |||
| - [《统计学习方法》的代码](https://gitee.com/afishoutis/MachineLearning) | |||
| - [《统计学习方法》pytorch实现](https://github.com/fengdu78/lihang-code) | |||
| - [pytorch-cifar100](https://github.com/weiaicunzai/pytorch-cifar100) 实现ResNet, DenseNet, VGG, GoogleNet, InceptionV3, InceptionV4, Inception-ResNetv2, Xception, Resnet In Resnet, ResNext,ShuffleNet, ShuffleNetv2, MobileNet, MobileNetv2, SqueezeNet, NasNet, Residual Attention Network, SENet, WideResNet | |||
| - [Attention: xmu-xiaoma666/External-Attention-pytorch: 🍀 Pytorch implementation of various Attention Mechanisms, MLP, Re-parameter, Convolution, which is helpful to further understand papers.⭐⭐⭐ (github.com)](https://github.com/xmu-xiaoma666/External-Attention-pytorch) 注意力机制，多层神经网络，重参数。 | |||
| - [Python TheAlgorithms/Python: All Algorithms implemented in Python (github.com)](https://github.com/TheAlgorithms/Python) | |||
| - PytTorch 训练手册 https://github.com/zergtant/pytorch-handbook | |||
| ## Exercise | |||
| ## 3. 练习 | |||
| * http://sofasofa.io/competitions.php?type=practice | |||
| * https://www.kaggle.com/competitions | |||
| * Machine learning project ideas | |||
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| * Python 小项目 https://github.com/kyclark/tiny_python_projects | |||
| ## Method | |||
| ## 4. 机器学习方法 | |||
| ### 4.1 经典机器学习方法 | |||
| * Programming Multiclass Logistic Regression | |||
| notebooks/MachineLearningNotebooks/05.%20Logistic%20Regression.ipynb | |||
| @@ -86,7 +90,7 @@ http://localhost:8889/notebooks/machineLearning/10_digits_classification.ipynb | |||
| http://localhost:8889/notebooks/machineLearning/notebooks/01%20-%20Model%20Selection%20and%20Assessment.ipynb | |||
| ## NN | |||
| ### 4.2 NN | |||
| * 神经网络——梯度下降&反向传播 https://blog.csdn.net/skullfang/article/details/78634317 | |||
| * 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 | |||
| * 如何直观地解释 backpropagation 算法？ https://www.zhihu.com/question/27239198 | |||
| @@ -97,10 +101,10 @@ http://localhost:8889/notebooks/machineLearning/notebooks/01%20-%20Model%20Selec | |||
| * https://www.python-course.eu/neural_networks_with_python_numpy.php | |||
| ## k-Means | |||
| ### 4.3 k-Means | |||
| * [如何使用 Keras 实现无监督聚类](http://m.sohu.com/a/236221126_717210) | |||
| ## AutoEncoder (自编码/非监督学习) | |||
| ### 4.4 AutoEncoder (自编码/非监督学习) | |||
| * https://morvanzhou.github.io/tutorials/machine-learning/torch/4-04-autoencoder/ | |||
| * https://github.com/MorvanZhou/PyTorch-Tutorial/blob/master/tutorial-contents/404_autoencoder.py | |||
| * pytorch AutoEncoder 自编码 https://www.jianshu.com/p/f0929f427d03 | |||