You will learn how to build a successful machine learning project. If you aspire to be a technical leader in AI, and know how to set direction for your team's work, this course will show you how.
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课程信息
320,226 次近期查看
学生职业成果
36%
完成这些课程后已开始新的职业生涯
38%
通过此课程获得实实在在的工作福利
14%
加薪或升职
您将获得的技能
Machine LearningDeep LearningInductive TransferMulti-Task Learning
学生职业成果
36%
完成这些课程后已开始新的职业生涯
38%
通过此课程获得实实在在的工作福利
14%
加薪或升职
可分享的证书
完成后获得证书
100% 在线
立即开始，按照自己的计划学习。
第 3 门课程（共 5 门）
可灵活调整截止日期
根据您的日程表重置截止日期。
初级
完成时间大约为7 小时
建议：2 weeks of study, 3-4 hours/week
英语（English）
字幕：中文（繁体）, 中文（简体）, 韩语, 土耳其语（Turkish）, 英语（English）, 西班牙语（Spanish）
教学大纲 - 您将从这门课程中学到什么
完成时间为 2 小时
ML Strategy (1)
完成时间为 2 小时
13 个视频 （总计 100 分钟）, 1 个阅读材料, 1 个测验
13 个视频
Orthogonalization10分钟
Single number evaluation metric7分钟
Satisficing and Optimizing metric5分钟
Train/dev/test distributions6分钟
Size of the dev and test sets5分钟
When to change dev/test sets and metrics11分钟
Why human-level performance?5分钟
Avoidable bias6分钟
Understanding human-level performance11分钟
Surpassing human-level performance6分钟
Improving your model performance4分钟
Andrej Karpathy interview15分钟
1 个阅读材料
Machine Learning flight simulator2分钟
1 个练习
Bird recognition in the city of Peacetopia (case study)45分钟
完成时间为 3 小时
ML Strategy (2)
完成时间为 3 小时
11 个视频 （总计 132 分钟）
11 个视频
Cleaning up incorrectly labeled data13分钟
Build your first system quickly, then iterate6分钟
Training and testing on different distributions10分钟
Bias and Variance with mismatched data distributions18分钟
Addressing data mismatch10分钟
Transfer learning11分钟
Multi-task learning12分钟
What is end-to-end deep learning?11分钟
Whether to use end-to-end deep learning10分钟
Ruslan Salakhutdinov interview17分钟
1 个练习
Autonomous driving (case study)45分钟
审阅
4.8
来自STRUCTURING MACHINE LEARNING PROJECTS的热门评论
由 MG 提供Mar 30, 2020
It is very nice to have a very experienced deep learning practitioner showing you the "magic" of making DNN works. That is usually passed from Professor to graduate student, but is available here now.
由 EK 提供Dec 27, 2017
Loved the course, and the simulation was great. Doing an actual transfer learning programming exercise in TensorFlow could be an awesome addition.\n\nBest & thanks again for an awesome course!\n\nEric
由 AM 提供Nov 22, 2017
I learned so many things in this module. I learned that how to do error analysys and different kind of the learning techniques. Thanks Professor Andrew Ng to provide such a valuable and updated stuff.
由 DC 提供Mar 7, 2018
Going beyond the technical details, this part of the course goes into the high level view on how to direct your efforts in a ML project. Really enjoyable and useful. Thanks for making this available!
由 WG 提供Mar 18, 2019
Though it might not seem imminently useful, the course notes I've referred back to the most come from this class. This course is could be summarized as a machine learning master giving useful advice.
由 ZZ 提供Apr 6, 2018
A lot of concrete examples, including those in the lectures and in the tests. Gained some thoughts on how to manage a ML project. Thanks Andrew and deeplearning.ai for providing such a great course.
由 AP 提供Jun 20, 2019
Really a good course with mostly the theoretical knowledge on some aspects to reuse your model as well as some error analysis. Thoroughly taught with lots of real-life examples, thanks to Andrew Ng.
由 SK 提供Feb 19, 2019
This is the knowledge in which we will get from lots of experience only, but the andrew has shared in this course which might help us in future by saving a lot of time through this course experience
由 TR 提供Sep 21, 2018
This is a must course in the entire specialization. It covers the step by step procedure to approach and solve a problem. The case studies provided are real world problems which are so much helpful.
由 NI 提供Nov 10, 2017
Awesome course as always. The course teaches real world practical aspects of how to get started and navigate in the real world projects. The guidelines are actual learnings from years of experience.
由 YL 提供Nov 28, 2017
It's a great course! This course gave me a lot of new perspectives in constructing a machine learning project. Especially, the discussion of data distribution in the train/dev/test set is fantastic.
由 MB 提供Aug 24, 2017
Course is great although only in one case we have pdf's with additional lecture notes. They are more useful than ppt slides therefore it would be desirable to have them in other parts of the course
提供方
deeplearning.ai
deeplearning.ai is Andrew Ng's new venture which amongst others, strives for providing comprehensive AI education beyond borders.
关于 深度学习 专项课程
If you want to break into AI, this Specialization will help you do so. Deep Learning is one of the most highly sought after skills in tech. We will help you become good at Deep Learning.
In five courses, you will learn the foundations of Deep Learning, understand how to build neural networks, and learn how to lead successful machine learning projects. You will learn about Convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and more. You will work on case studies from healthcare, autonomous driving, sign language reading, music generation, and natural language processing. You will master not only the theory, but also see how it is applied in industry. You will practice all these ideas in Python and in TensorFlow, which we will teach.
You will also hear from many top leaders in Deep Learning, who will share with you their personal stories and give you career advice.
AI is transforming multiple industries. After finishing this specialization, you will likely find creative ways to apply it to your work.
We will help you master Deep Learning, understand how to apply it, and build a career in AI.
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