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Отзывы учащихся о курсе Structuring Machine Learning Projects от партнера

Оценки: 38,671
Рецензии: 4,232

О курсе

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....

Лучшие рецензии


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.


Mar 31, 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.

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3776–3800 из 4,200 отзывов о курсе Structuring Machine Learning Projects

автор: Lei C

Sep 25, 2017

the answer of the assignment is a little bit arguable.

автор: diego s

Feb 18, 2020

I miss notebooks for practice, besides questionnaires

автор: Xinghua J

Sep 06, 2019

If there is some coding practice, it would be better

автор: Hee S K

Apr 18, 2018

It is an unique lecture providing empirical advises.

автор: Pablo L

Oct 30, 2017

Great set of guidelines. Mostly theoretical, though.

автор: Cristina G

Oct 22, 2017

Concrete reminders of important and practical points

автор: Ktawut T

Oct 10, 2017

Very useful materials for leading a ML research team

автор: awalin s

Sep 29, 2017

interesting insights about real world implementation

автор: Yu L

Apr 03, 2020

would like to have more excercise related to coding

автор: Mage K

Mar 07, 2018

Would've liked to have some programming assignments

автор: Carlisle

Aug 20, 2017

Introduced a lot on engineering project experiences

автор: William L

Apr 17, 2020

Very useful knowledge that is not commonly taught.

автор: Alvaro G d P

Nov 27, 2017

Interesting but perhaps we could have gone deeper.

автор: John H

Aug 26, 2017

Is the flight simulator hw going to be added soon?

автор: Pat B

Dec 08, 2019

Great course. I liked the compact, 2-week format.

автор: liu c

Mar 17, 2018

A little bit abstract. But still very inspiring!

автор: Florian M

Aug 24, 2017

Very interesting tools and ideas for applied ML.

автор: Jason G

Nov 25, 2018

Not as strong as the other 4 of 5 of the series

автор: Mark

Oct 13, 2018

Great course. Needs deeper practical examples.

автор: Francis J

Feb 25, 2018

A lot of insights rather than technical details

автор: Lukáš L

Jan 07, 2018

Coding exercises would be great in this course.

автор: Tulip T

Jul 23, 2019

Quite helpful when you start a new ML project.

автор: S V R

Nov 05, 2018

The session were simple, could be more complex

автор: Caique D S C

Jul 31, 2018

very good course, could be less massive though

автор: Виницкий И В

Dec 11, 2019

I want a program exercise like in 1-2 courses