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3.4 kB
Python与机器学习
本教程包含了一些使用Python来学习机器学习的notebook，通过本教程能够引导学习Python的基础知识和机器学习的理论知识和实际编程，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，因此需要认真把作业和报告完成。作业的地址是：https://gitee.com/machinelearning2018/pr_homework 请按照里面的说明进行操作。
内容
- Python
- numpy & matplotlib
- knn
- kMenas
- Logistic Regression
- Neural Network
- PyTorch
- short tutorial
- basic/Tensor-and-Variable
- basic/autograd
- basic/dynamic-graph
- nn/linear-regression-gradient-descend
- nn/logistic-regression
- nn/nn-sequential-module
- nn/bp
- nn/deep-nn
- nn/param_initialize
- optim/sgd
- optim/adam
- optim/adam
- cnn/basic_conv
- cnn/batch-normalization
- cnn/regularization
- cnn/lr-decay
- cnn/vgg
- cnn/googlenet
- cnn/densenet
- cnn/resnet
- rnn/pytorch-rnn
- rnn/rnn-for-image
- rnn/lstm-time-series
- gan/autoencoder
- gan/vae
- gan/gan