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

1.1 教程

• 《动手学深度学习》- PyTorch版本
• Introduction — Neuromatch Academy: Deep Learning
• Pytorch 常用代码合集
  • 主要包括如下功能：
  • 导入包和版本查询
  • 单张显卡设置以及多张显卡设置
  • 清除显存
  • 张量处理
  • 计算两组数据之间的欧式距离
  • 模型的定义
  • 模型参数量的计算
  • 模型的可视化
  • 将GPU的数据加载到CPU
  • 数据集均方和标准差的处理
  • 模型的训练与测试demo
  • 模型的保存与断点加载

1.2 代码

• 《统计学习方法》的代码
• 《统计学习方法》PyTorch实现
• 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) 注意力机制，多层神经网络，重参数。
• Python TheAlgorithms/Python: All Algorithms implemented in Python (github.com)
• 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
• 梯度下降方法的视觉解释（动量，AdaGrad，RMSProp，Adam）
• 5种常用的交叉验证技术，保证评估模型的稳定性
• The Neural network zoo : 各种神经网络架构的解释、图示
• 22 个神经网络结构设计/可视化工具
  • https://www.toutiao.com/i6836884346155041292/

• https://github.com/ashishpatel26/Tools-to-Design-or-Visualize-Architecture-of-Neural-Network

• CNN 可视化工具 https://m.toutiaocdn.com/group/6822123587156050435

  • https://poloclub.github.io/cnn-explainer/
  • https://github.com/poloclub/cnn-explainer

• 打标签工具

  • Label Studio
    • Demo video https://www.bilibili.com/video/BV1dL41147KE
    • Documents https://labelstud.io/guide/
  • LabelImg

• 一款图像转卡通的Python项目，超级值得你练手

  • https://www.toutiao.com/a6821299115175969287/
  • https://github.com/minivision-ai/photo2cartoon

• Awesome Deep Learning Project Ideas

• Machine Learning From Scratch

3. 练习

• http://sofasofa.io/competitions.php?type=practice

• https://www.kaggle.com/competitions

• Machine learning project ideas

  • https://data-flair.training/blogs/machine-learning-project-ideas/
  • https://data-flair.training/blogs/deep-learning-project-ideas/
  • https://www.kdnuggets.com/2020/03/20-machine-learning-datasets-project-ideas.html

• Titanic: notebooks/data-science-ipython-notebooks/kaggle/titanic.ipynb

• 使用神经网络解决拼图游戏 https://www.toutiao.com/a6855437347463365133/

• Sudoku-Solver

• Python 小项目 https://github.com/kyclark/tiny_python_projects

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

• Equation for MLP notebooks/MachineLearningNotebooks/07.%20MLP%20Neural%20Networks.ipynb

• Optimization methods notebooks/MachineLearningNotebooks/06.%20Optimization.ipynb

• 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

• evaluation metrics http://localhost:8889/notebooks/machineLearning/10_digits_classification.ipynb

• model selection and assessment http://localhost:8889/notebooks/machineLearning/notebooks/01%20-%20Model%20Selection%20and%20Assessment.ipynb

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

• 一文弄懂神经网络中的反向传播法——BackPropagation https://www.cnblogs.com/charlotte77/p/5629865.html

• https://medium.com/@UdacityINDIA/how-to-build-your-first-neural-network-with-python-6819c7f65dbf

• https://enlight.nyc/projects/neural-network/

• https://www.python-course.eu/neural_networks_with_python_numpy.php

4.3 k-Means

• 如何使用 Keras 实现无监督聚类

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
• Adversarial Autoencoders (with Pytorch) https://blog.paperspace.com/adversarial-autoencoders-with-pytorch/