← 开源
aws-samples

machine-learning-samples

Sample applications built using AWS' Amazon Machine Learning.

TutorialsTool-specific tutorialsPython
在 GitHub 打开
增长势头
+024 小时新增 Star0.0%
879
Star
387
Fork
+-1
本周
14
贡献者
创建于 2015-03-24 · 更新于 2026-10-10 · 今日第 12956 名
主要开发者
leopdfilippovitalecsanchiz

aws-samples/machine-learning-samples 排名第 12,956,有 879 个星标。

同类别比较

aws-samples/machine-learning-samples 排名第 12,956,此处有 8 个同类条目;logancyang/my-cs-degree 排名第 12,958。

同类别比较
名称排名指标
amusi/PyTorch-From-Zero-To-One12,913
星标数:
884
分叉数:
219
简介:
PyTorch从入门到精通
openai/openai-chatkit-starter-app12,918
语言:
Python
星标数:
884
分叉数:
1,160
简介:
Starter app to build with OpenAI ChatKit + Agent Builder
oreilly-japan/deep-learning-from-scratch-312,925
语言:
Python
星标数:
883
分叉数:
337
简介:
『ゼロから作る Deep Learning ❸』(O'Reilly Japan, 2020)
alexgkendall/SegNet-Tutorial12,946
语言:
Python
星标数:
880
分叉数:
512
简介:
Files for a tutorial to train SegNet for road scenes using the CamVid dataset
logancyang/my-cs-degree12,958
星标数:
879
分叉数:
138
简介:
A CS degree with a focus on full-stack ML engineering, 2020
openai/openai-responses-starter-app12,963
语言:
TypeScript
星标数:
878
分叉数:
305
简介:
Starter app to build with the OpenAI Responses API
ritchieng/deep-learning-wizard12,964
语言:
Python
星标数:
878
分叉数:
234
简介:
Open source guides/codes for mastering deep learning to deploying deep learning in production in PyTorch, Python, Apptainer, and more.
Edward1Chou/SentimentAnalysis12,972
语言:
Jupyter Notebook
星标数:
876
分叉数:
226
简介:
文本情感分析
关键参数与规格
关键参数与规格
参数值
语言Python
类别Tutorials
子类别Tool-specific tutorials
星标数879
分叉数387
贡献者14
创建日期2015-03-24
更新日期2026-10-10
1 天星标数0
7 天星标数-1
排名12,956
上次排名12,852
排名变化-104
7 天星标排名17,485
上次 7 天星标排名17,451
7 天星标排名变化-34
总星标排名12,329
上次总星标排名12,304
总星标排名变化-25
国家USA
README 摘录

Amazon Machine Learning Samples Each subdirectory contains sample code for using Amazon Machine Learning. Refer to the file in each sub-directory for details on using each sample. Targeted Marketing Samples These samples show how to use the Amazon Machine Learning API for a targeted marketing application. This follows the "banking" dataset example described in the Developer Guide. There are three versions available: Targeted Marketing with Machine Learning in Java Targeted Marketing with Machine Learning in Python Targeted Marketing with

Machine Learning in Scala Social Media and Amazon Mechanical Turk This sample application shows how to use Amazon Mechanical Turk to create a labeled dataset from raw tweets, and then build a machine learning model using the Amazon Machine Learning API that predicts whether or not new tweets should be acted upon by customer service. The sample shows how to set up an automated filter using AWS Lambda that monitors tweets on an Amazon Kinesis stream and sends notifications whenever

the ML Model predicts that a new tweet is actionable. Notifications go to Amazon SNS, allowing delivery to email, SMS text messages, or other software services. Machine-Learning based Social Media Filtering (Python & JavaScript) Mobile Prediction Samples These samples show how to use the Amazon Machine Learning API to make real-time predictions from a mobile device. There are two versions available: Real-time Machine Learning Predictions from iOS Real-time Machine Learning Predictions from Android K-fold Cross-validation

Sample This sample shows how to use the Amazon Machine Learning API to evaluate ML models using k-fold cross-validation. K-fold Cross-validation Sample (Python) Other tools A collection of simple scripts to help with common tasks. Machine Learning Tools (python) Support For assistance with using the Amazon Machine Learning Service, or these samples, please see the AWS Forums.

常见问题

记录了多少星标和分叉?

aws-samples/machine-learning-samples 有 879 个星标和 387 个分叉。

仓库排名是多少?

aws-samples/machine-learning-samples 排名第 12,956。

记录的语言和类别是什么?

aws-samples/machine-learning-samples 有 879 个星标,语言为 Python,类别为 Tutorials。

哪个仓库排名最接近?

aws-samples/machine-learning-samples 与 logancyang/my-cs-degree 比较,后者排名第 12,958,有 879 个星标。

README

Amazon Machine Learning Samples

Each subdirectory contains sample code for using Amazon Machine Learning. Refer to the README.md file in each sub-directory for details on using each sample.

Targeted Marketing Samples

These samples show how to use the Amazon Machine Learning API for a targeted marketing application. This follows the "banking" dataset example described in the Developer Guide. There are three versions available:

Social Media and Amazon Mechanical Turk

This sample application shows how to use Amazon Mechanical Turk to create a labeled dataset from raw tweets, and then build a machine learning model using the Amazon Machine Learning API that predicts whether or not new tweets should be acted upon by customer service. The sample shows how to set up an automated filter using AWS Lambda that monitors tweets on an Amazon Kinesis stream and sends notifications whenever the ML Model predicts that a new tweet is actionable. Notifications go to Amazon SNS, allowing delivery to email, SMS text messages, or other software services.

Mobile Prediction Samples

These samples show how to use the Amazon Machine Learning API to make real-time predictions from a mobile device. There are two versions available:

K-fold Cross-validation Sample

This sample shows how to use the Amazon Machine Learning API to evaluate ML models using k-fold cross-validation.

Other tools

A collection of simple scripts to help with common tasks.

Support

For assistance with using the Amazon Machine Learning Service, or these samples, please see the AWS Forums.

排名相邻
同类项目