谷歌中国开发者社区 (GDG)
  • 主页
  • 博客
    • Android
    • Design
    • GoogleCloud
    • GoogleMaps
    • GooglePlay
    • Web
  • 社区
    • 各地社区
    • 社区历史
    • GDG介绍
    • 社区通知
  • 视频
  • 资源
    • 资源汇总
    • 精选视频
    • 优酷频道

Machine Learning Crash Course

2018-03-03adminGoogleDevFeedsNo comments

Posted by Barry Rosenberg, Google Engineering Education Team

Today, we’re happy to share our Machine Learning Crash Course (MLCC) with the world. MLCC is one of the most popular courses created for Google engineers. Our engineering education team has delivered this course to more than 18,000 Googlers, and now you can take it too! The course develops intuition around fundamental machine learning concepts.

What does the course cover?

MLCC covers many machine learning fundamentals, starting with loss and gradient descent, then building through classification models and neural nets. The programming exercises introduce TensorFlow. You’ll watch brief videos from Google machine learning experts, read short text lessons, and play with educational gadgets devised by instructional designers and engineers.

How much does it cost?

MLCC is free.

I don’t get it. Why are you offering MLCC to everyone?

We believe that the potential of machine learning is so vast that every technical person should learn machine learning fundamentals. We’re offering the course in English, Spanish, Korean, Mandarin, and French.

Does the real world make an appearance in the course?

Yes, MLCC ends with short lessons on designing real-world machine learning systems. MLCC also contains sections enabling you to learn from the mistakes that our experts have made.

Do I have enough mathematical background to understand MLCC?

Understanding a little algebra and a little elementary statistics (mean and standard deviation) is helpful. If you understand calculus, you’ll get a bit more out of the course, but calculus is not a requirement. MLCC contains a helpful section to refresh your memory on the background math.

Is this a programming course?

MLCC contains some Python programming exercises. However, those exercises comprise only a small percentage of the course, which non-programmers may safely skip.

I’m new to Python. Will the programming exercises be too hard for me?

Many of the Google engineers who took MLCC didn’t know any Python but still completed the exercises. That’s because you’ll write only a few lines of code during the programming exercises. Instead of writing code from scratch, you’ll primarily manipulate the values of existing variables. That said, the code will be easier to understand if you can program in Python.

But how will I learn machine learning concepts without programming?

MLCC relies on a variety of media and hands-on interactive tools to build intuition in fundamental machine learning concepts. You need a technical mind, but you don’t need programming skills.

How can I show off my machine learning skills?

As your knowledge about Machine Learning grows, you can test your skill by helping others. We’re also kicking off a Kaggle competition to help DonorsChoose.org. DonorsChoose.org is an organization that empowers public school teachers from across the country to request materials and experiences they need to help their students grow. Teachers submit hundreds of thousands of project proposals each year; 500,000 proposals are expected in 2018.

Currently, DonorsChoose.org relies on a large number of volunteers to screen the proposals. The Kaggle competition hopes to help DonorsChoose.org use ML to accelerate the screening process, which will enable volunteers to make better use of their time. In addition, this work should help increase the consistency of decisions about projects.

Is MLCC Google’s only machine learning educational project?

MLCC is merely one of many ways to learn about machine learning. To explore the universe of machine learning educational opportunities from Google, see our new Learn with Google AI program at g.co/learnwithgoogleai. To start on MLCC, see g.co/machinelearningcrashcourse.



Source: Machine Learning Crash Course

除非特别声明,此文章内容采用知识共享署名 3.0许可,代码示例采用Apache 2.0许可。更多细节请查看我们的服务条款。

Tags: Develop

Related Articles

Funding 15,000 web and android scholarship in Africa – to provide employable developer skills

2018-03-24admin

DeepVariant Accuracy Improvements for Genetic Datatypes

2018-04-20admin

Google-Landmarks: A New Dataset and Challenge for Landmark Recognition

2018-03-02admin

Leave a Reply Cancel reply

Your email address will not be published. Required fields are marked *

You may use these HTML tags and attributes: <a href="" title=""> <abbr title=""> <acronym title=""> <b> <blockquote cite=""> <cite> <code class="" title="" data-url=""> <del datetime=""> <em> <i> <q cite=""> <s> <strike> <strong> <pre class="" title="" data-url=""> <span class="" title="" data-url="">

Recent Posts

  • TensorRT 与 TensorFlow 1.7 集成
  • AIY 项目:2018 更新套件
  • 投资法国的 AI 生态系统
  • Google’s Workshop on AI/ML Research and Practice in India
  • Time to celebrate the 2018 Google Play Award nominees

Recent Comments

  • 鸿维 on Google 帐号登录 API 更新
  • admin on 推出 CVPR 2018 学习图像压缩挑战赛
  • Henry Chen on 推出 CVPR 2018 学习图像压缩挑战赛
  • 王中 on Google 推出的 31 套在线课程
  • Francis Wang on Google 推出的 31 套在线课程

Archives

  • April 2018
  • March 2018
  • February 2018
  • January 2018
  • December 2017
  • November 2017
  • October 2017
  • September 2017
  • August 2017
  • July 2017
  • June 2017
  • May 2017
  • April 2017
  • March 2017
  • February 2017
  • January 2017
  • December 2016
  • November 2016
  • October 2016
  • September 2016
  • August 2016
  • May 2016
  • April 2016
  • March 2016
  • February 2016
  • January 2016
  • December 2015
  • November 2015
  • October 2015
  • September 2015
  • August 2015
  • July 2015
  • June 2015
  • January 1970

Categories

  • Android
  • Design
  • Firebase
  • GoogleCloud
  • GoogleDevFeeds
  • GoogleMaps
  • GooglePlay
  • Google动态
  • iOS
  • Uncategorized
  • VR
  • Web
  • WebMaster
  • 社区
  • 通知

Meta

  • Register
  • Log in
  • Entries RSS
  • Comments RSS
  • WordPress.org

最新文章

  • TensorRT 与 TensorFlow 1.7 集成
  • AIY 项目:2018 更新套件
  • 投资法国的 AI 生态系统
  • Google’s Workshop on AI/ML Research and Practice in India
  • Time to celebrate the 2018 Google Play Award nominees
  • Introducing Partner Interconnect, a fast, economical onramp to GCP
  • Improved code caching
  • 通过机器学习发现神经网络优化器
  • Rolling out the red carpet for GSoC 2018 students!
  • Introducing the CVPR 2018 On-Device Visual Intelligence Challenge

最多查看

  • 谷歌招聘软件工程师 (19,918)
  • Google 推出的 31 套在线课程 (18,087)
  • 如何选择 compileSdkVersion, minSdkVersion 和 targetSdkVersion (14,903)
  • Seti UI 主题: 让你编辑器焕然一新 (11,117)
  • Android Studio 2.0 稳定版 (8,419)
  • Android N 最初预览版:开发者 API 和工具 (7,752)
  • 像 Sublime Text 一样使用 Chrome DevTools (5,611)
  • Google I/O 2016: Android 演讲视频汇总 (5,387)
  • 用 Google Cloud 打造你的私有免费 Git 仓库 (4,896)
  • 面向普通开发者的机器学习应用方案 (4,734)
  • 生还是死?Android 进程优先级详解 (4,709)
  • 面向 Web 开发者的 Sublime Text 插件 (4,002)
  • 适配 Android N 多窗口特性的 5 个要诀 (3,838)
  • 参加 Google I/O Extended,观看 I/O 直播,线下聚会! (3,419)
© 2018 中国谷歌开发者社区 - ChinaGDG