文件列表:

LICENSE (1064, 2022-06-27)
Moon_Data (0, 2022-06-27)
Moon_Data\Moon_Classification_Exercise.ipynb (20433, 2022-06-27)
Moon_Data\Moon_Classification_Solution.ipynb (133626, 2022-06-27)
Moon_Data\source (0, 2022-06-27)
Moon_Data\source\model.py (842, 2022-06-27)
Moon_Data\source\predict.py (2485, 2022-06-27)
Moon_Data\source\train.py (6254, 2022-06-27)
Moon_Data\source_solution (0, 2022-06-27)
Moon_Data\source_solution\model.py (1160, 2022-06-27)
Moon_Data\source_solution\predict.py (2485, 2022-06-27)
Moon_Data\source_solution\train.py (6809, 2022-06-27)
Payment_Fraud_Detection (0, 2022-06-27)
Payment_Fraud_Detection\Fraud_Detection_Exercise.ipynb (33743, 2022-06-27)
Payment_Fraud_Detection\Fraud_Detection_Solution.ipynb (1053644, 2022-06-27)
Payment_Fraud_Detection\notebook_ims (0, 2022-06-27)
Payment_Fraud_Detection\notebook_ims\fraud_detection.png (155932, 2022-06-27)
Payment_Fraud_Detection\notebook_ims\linear_separator.png (32218, 2022-06-27)
Payment_Fraud_Detection\notebook_ims\precision_recall.png (74447, 2022-06-27)
Population_Segmentation (0, 2022-06-27)
Population_Segmentation\Pop_Segmentation_Exercise.ipynb (98944, 2022-06-27)
Population_Segmentation\Pop_Segmentation_Solution.ipynb (342538, 2022-06-27)
Population_Segmentation\notebook_ims (0, 2022-06-27)
Population_Segmentation\notebook_ims\3d_original_data.png (26975, 2022-06-27)
Population_Segmentation\notebook_ims\elbow_graph.png (97192, 2022-06-27)
Population_Segmentation\notebook_ims\pca_2d_dim_reduction.png (21123, 2022-06-27)
Project_Plagiarism_Detection (0, 2022-06-27)
Project_Plagiarism_Detection\1_Data_Exploration-zh.ipynb (9019, 2022-06-27)
Project_Plagiarism_Detection\2_Plagiarism_Feature_Engineering.ipynb (51763, 2022-06-27)
Project_Plagiarism_Detection\3_Training_a_Model.ipynb (18820, 2022-06-27)
Project_Plagiarism_Detection\helpers.py (4941, 2022-06-27)
Project_Plagiarism_Detection\notebook_ims (0, 2022-06-27)
Project_Plagiarism_Detection\notebook_ims\common_subseq_words.png (134996, 2022-06-27)
Project_Plagiarism_Detection\notebook_ims\matrix_1.png (35050, 2022-06-27)
Project_Plagiarism_Detection\notebook_ims\matrix_2.png (26600, 2022-06-27)
Project_Plagiarism_Detection\notebook_ims\matrix_3_match.png (24983, 2022-06-27)
Project_Plagiarism_Detection\notebook_ims\matrix_6_complete.png (24956, 2022-06-27)
... ...

# 使用 AWS SageMaker 部署机器学习模型的案例研究 此代码库包含使用 AWS SageMaker 部署机器学习模型的代码和相关文件。其中包含各种案例研究、代码练习和项目文件的多个教程 notebook,展示了机器学习工作流程的各个环节,并使你有机会练习部署各种机器学习算法。 ### 教程 * [人口分割](https://github.com/udacity/ML_SageMaker_Studies/tree/master/Population_Segmentation):学习如何在 SageMaker 中构建和部署非监督式模型。在此示例中,你需要聚类美国人口普查数据:使用 PCA 降低数据的维度,并通过 k 均值对生成的主成分进行聚类。 * [支付欺诈检测](https://github.com/udacity/ML_SageMaker_Studies/tree/master/Payment_Fraud_Detection):学习如何在 SageMaker 中构建和部署监督式 LinearLearner 模型。你需要优化模型并处理类别不平衡性问题,然后训练模型检测信用卡欺诈行为。 * [部署自定义 PyTorch 模型 (Moon Data)](https://github.com/udacity/ML_SageMaker_Studies/tree/master/Moon_Data):训练和部署一个分类“月亮”数据的自定义 PyTorch 神经网络,“月亮”数据是形状像月亮一样的二维数据集。 * [时间序列预测](https://github.com/udacity/ML_SageMaker_Studies/tree/master/Time_Series_Forecasting):学习分析时间序列数据,并调整数据格式,使其能够用于训练 [DeepAR](https://docs.aws.amazon.com/sagemaker/latest/dg/deepar.html) 算法,这是一种利用循环神经网络的预测算法。你需要训练一个模型来预测家庭能耗模式并评估结果。 ### 实战项目 [剽窃检测器](https://github.com/udacity/ML_SageMaker_Studies/tree/master/Project_Plagiarism_Detection):构建一个端到端剽窃分类模型。运用所学的技能清理数据、提取有意义的特征,并在 SageMaker 中部署剽窃分类器。  --- ## 设置说明 此代码库中提供的 notebook 需要使用 Amazon SageMaker 平台执行。下面简要说明了如何使用 SageMaker 设置托管 notebook 实例,你可以在此实例中完成和运行 notebook。 ### 登录 AWS 控制台并创建一个 notebook 实例 登录 [AWS 控制台](https://console.aws.amazon.com)并转到 SageMaker 信息中心。点击“Create notebook instance”。 * notebook 可以随意命名,建议使用 ml.t2.medium,因为它属于免费套餐。 * 对于角色,新建一个角色就行了。使用默认选项即可。 * 注意,notebook 实例需要能够访问 S3 资源,默认就能访问。该 notebook 可以访问名称中带 sagemaker 的任何 S3 存储桶或对象。 * 使用 **git clone** 将项目代码库克隆到 notebook 实例中,网址为:`https://github.com/udacity/CN-ML_SageMaker_Studies.git` ### 打开和运行你所选的 notebook 将代码库克隆到 notebook 实例中后,你可以转到要完成或执行的任何 notebook,然后完成该 notebook。每个 notebook 都包含了额外的说明。 # Archival Note This repository is deprecated; therefore, we are going to archive it. However, learners will be able to fork it to their personal Github account but cannot submit PRs to this repository. If you have any issues or suggestions to make, feel free to: - Utilize the https://knowledge.udacity.com/ forum to seek help on content-specific issues. - Submit a support ticket along with the link to your forked repository if (learners are) blocked for other reasons. Here are the links for the [retail consumers](https://udacity.zendesk.com/hc/en-us/requests/new) and [enterprise learners](https://udacityenterprise.zendesk.com/hc/en-us/requests/new?ticket_form_id=360000279131).

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