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Structuring Machine Learning Projects(으)로 돌아가기

Structuring Machine Learning Projects, deeplearning.ai

4.8
(27,051개의 평가)

About this Course

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. Much of this content has never been taught elsewhere, and is drawn from my experience building and shipping many deep learning products. This course also has two "flight simulators" that let you practice decision-making as a machine learning project leader. This provides "industry experience" that you might otherwise get only after years of ML work experience. After 2 weeks, you will: - Understand how to diagnose errors in a machine learning system, and - Be able to prioritize the most promising directions for reducing error - Understand complex ML settings, such as mismatched training/test sets, and comparing to and/or surpassing human-level performance - Know how to apply end-to-end learning, transfer learning, and multi-task learning I've seen teams waste months or years through not understanding the principles taught in this course. I hope this two week course will save you months of time. This is a standalone course, and you can take this so long as you have basic machine learning knowledge. This is the third course in the Deep Learning Specialization....

최상위 리뷰

대학: AM

Nov 23, 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.

대학: WG

Mar 19, 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.

필터링 기준:

2,860개의 리뷰

대학: Aru

May 22, 2019

very good

대학: Parth Sankhavara

May 22, 2019

This course is absolutely for who is coming out to structure the projects and research the tuning the Machine learning models.

대학: Jingxiao Zhang

May 21, 2019

This is a practical course, extremely helpful for those who have met so many troubles in realworld projects. It is quite helpful for startups, where we can implement those ideas immediately. On the other hand, the transfer learning and end-to-end learning paradigms might be very useful but challeging in big companies and sectors.

대학: Khaled Jafar

May 20, 2019

Excellent class with practical advise to accelerate the application of best practices based on Andrew's experience. I would highly recommend this to practitioners wanting to save a lot of time learning these best practices the hard way.

대학: Hasroyan

May 20, 2019

Flight Simulators look very efficient

대학: Bernard Leong

May 20, 2019

Useful for those who are thinking of ways to debug their AI projects and also learning methods to deal with errors, biases and treating datasets.

대학: Ayon Banerjee

May 19, 2019

Very good indeed. The methods shared are rarely found in books.

대학: John Schneider

May 19, 2019

I like the "flight simulator" quizzes a lot and other courses might benefit from a similar assessment (in addition to regular quizzes and programming exercises), but I do think this course would benefit from some programming exercises too. Thanks!

대학: Sanika Awasthi

May 19, 2019

loved this course...

대학: Rashmi Nagpal

May 19, 2019

Thanks a real bunch, Coursera for providing financial aid and bringing up this course, truly loved each and every section, coupled with quiz section at the end, is so much helpful and of course, very thoroughly made! Thanks to all the hardworking instructors and teaching assistance, and of course, coursera team for making this course so effectively! :)