# 机器学习 本教程主要讲解机器学习的基本原理与实现，通过本教程的引导来快速学习Python、Python常用库、机器学习的理论知识与实际编程，并学习如何解决实际问题。 由于**本课程需要大量的编程练习才能取得比较好的学习效果**，因此需要认真去完成[作业和报告](https://gitee.com/pi-lab/machinelearning_homework)，写作业的过程可以查阅网上的资料，但是不能直接照抄，需要自己独立思考并独立写出代码。 ## 1. 内容 1. [课程简介](CourseIntroduction.pdf) 2. [Python](0_python/) - [Install Python](tips/InstallPython.md) - [Introduction](0_python/0_Introduction.ipynb) - [Python Basics](0_python/1_Basics.ipynb) - [Print Statement](0_python/2_Print_Statement.ipynb) - [Data Structure 1](0_python/3_Data_Structure_1.ipynb) - [Data Structure 2](0_python/4_Data_Structure_2.ipynb) - [Control Flow](0_python/5_Control_Flow.ipynb) - [Function](0_python/6_Function.ipynb) - [Class](0_python/7_Class.ipynb) 3. [numpy & matplotlib](1_numpy_matplotlib_scipy_sympy/) - [numpy](1_numpy_matplotlib_scipy_sympy/numpy_tutorial.ipynb) - [matplotlib](1_numpy_matplotlib_scipy_sympy/matplotlib_simple_tutorial.ipynb) - [ipython & notebook](1_numpy_matplotlib_scipy_sympy/ipython_notebook.ipynb) 4. [knn](2_knn/knn_classification.ipynb) 5. [kMenas](3_kmeans/k-means.ipynb) 6. [Logistic Regression](4_logistic_regression/) - [Least squares](4_logistic_regression/Least_squares.ipynb) - [Logistic regression](4_logistic_regression/Logistic_regression.ipynb) 7. [Neural Network](5_nn/) - [Perceptron](5_nn/Perceptron.ipynb) - [Multi-layer Perceptron & BP](5_nn/mlp_bp.ipynb) - [Softmax & cross-entroy](5_nn/softmax_ce.ipynb) 8. [PyTorch](6_pytorch/) - Basic - [short tutorial](6_pytorch/PyTorch_quick_intro.ipynb) - [basic/Tensor-and-Variable](6_pytorch/0_basic/Tensor-and-Variable.ipynb) - [basic/autograd](6_pytorch/0_basic/autograd.ipynb) - [basic/dynamic-graph](6_pytorch/0_basic/dynamic-graph.ipynb) - NN & Optimization - [nn/linear-regression-gradient-descend](6_pytorch/1_NN/linear-regression-gradient-descend.ipynb) - [nn/logistic-regression](6_pytorch/1_NN/logistic-regression.ipynb) - [nn/nn-sequential-module](6_pytorch/1_NN/nn-sequential-module.ipynb) - [nn/bp](6_pytorch/1_NN/bp.ipynb) - [nn/deep-nn](6_pytorch/1_NN/deep-nn.ipynb) - [nn/param_initialize](6_pytorch/1_NN/param_initialize.ipynb) - [optim/sgd](6_pytorch/1_NN/optimizer/sgd.ipynb) - [optim/adam](6_pytorch/1_NN/optimizer/adam.ipynb) - CNN - [CNN simple demo](demo_code/3_CNN_MNIST.py) - [cnn/basic_conv](6_pytorch/2_CNN/basic_conv.ipynb) - [cnn/minist (demo code)](./demo_code/3_CNN_MNIST.py) - [cnn/batch-normalization](6_pytorch/2_CNN/batch-normalization.ipynb) - [cnn/regularization](6_pytorch/2_CNN/regularization.ipynb) - [cnn/lr-decay](6_pytorch/2_CNN/lr-decay.ipynb) - [cnn/vgg](6_pytorch/2_CNN/vgg.ipynb) - [cnn/googlenet](6_pytorch/2_CNN/googlenet.ipynb) - [cnn/resnet](6_pytorch/2_CNN/resnet.ipynb) - [cnn/densenet](6_pytorch/2_CNN/densenet.ipynb) - RNN - [rnn/pytorch-rnn](6_pytorch/3_RNN/pytorch-rnn.ipynb) - [rnn/rnn-for-image](6_pytorch/3_RNN/rnn-for-image.ipynb) - [rnn/lstm-time-series](6_pytorch/3_RNN/time-series/lstm-time-series.ipynb) - GAN - [gan/autoencoder](6_pytorch/4_GAN/autoencoder.ipynb) - [gan/vae](6_pytorch/4_GAN/vae.ipynb) - [gan/gan](6_pytorch/4_GAN/gan.ipynb) ## 2. 其他参考资料 * 资料速查 * [相关学习参考资料汇总](References.md) * [一些速查手册](tips/cheatsheet) * 机器学习方面技巧等 * [Confusion Matrix](tips/confusion_matrix.ipynb) * [Datasets](tips/datasets.ipynb) * [构建深度神经网络的一些实战建议](tips/构建深度神经网络的一些实战建议.md) * [Intro to Deep Learning](tips/Intro_to_Deep_Learning.pdf) * Python技巧等 * [安装Python环境](tips/InstallPython.md) * [Python tips](tips/python) * Git * [Git Tips - 常用方法速查，快速入门](https://gitee.com/pi-lab/learn_programming/blob/master/6_tools/git/git-tips.md) * [Git快速入门 - Git初体验](https://my.oschina.net/dxqr/blog/134811) * [在win7系统下使用TortoiseGit(乌龟git)简单操作Git](https://my.oschina.net/longxuu/blog/141699) * [Git系统学习 - 廖雪峰的Git教程](https://www.liaoxuefeng.com/wiki/0013739516305929606dd18361248578c67b8067c8c017b000) * Markdown * [Markdown——入门指南](https://www.jianshu.com/p/1e402922ee32)