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Comentários e feedback de alunos de Structuring Machine Learning Projects da instituição deeplearning.ai

4.8
estrelas
44,188 classificações
4,982 avaliações

Sobre o curso

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....

Melhores avaliações

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.

JB

Jul 02, 2020

While the information from this course was awesome I would've liked some hand on projects to get the information running. Nonetheless, the two simulation task were the best (more would've been neat!).

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4601 — 4625 de 4,930 Avaliações para o Structuring Machine Learning Projects

por Predrag S

Mar 10, 2018

Very good.

por Johannes L

Aug 29, 2017

Excellent!

por Luis A P

Jul 02, 2020

very nice

por Yechan K

Jun 17, 2020

Practical

por IURII B

Apr 05, 2018

Thank you

por Abhijeet R P

Oct 19, 2017

Great! :)

por 舒意恒

Oct 14, 2017

very nice

por TianPing

Aug 27, 2017

内容稍稍有点重复。

por Dave

Jul 10, 2020

verygood

por Yashika S

Sep 27, 2019

good one

por Xiong Z

Sep 04, 2019

helpeful

por M N N

May 28, 2019

Awesome!

por mingwei Z

Sep 06, 2018

so well

por 靳雅麟

Dec 23, 2017

没有中文字幕

por Tất T V

Oct 15, 2017

Useful

por Takuya Kudo

Aug 10, 2019

Cool.

por Riyaz A

Sep 22, 2017

g

r

e

a

t

por akash k

Aug 13, 2020

Good

por Alaa E B

Jun 23, 2020

good

por CK P D

May 02, 2020

Good

por Annaluru K

Apr 17, 2020

Well

por VIGNESHKUMAR R

Oct 23, 2019

good

por zhesihuang

Mar 03, 2019

good

por CARLOS G G

Jul 08, 2018

good

por Felix E

Oct 09, 2017

This is a 2-week follow-up on the previous two courses in this specialization.

While it's a decent course that goes over a few interesting topics, I have a hard time giving it more than three stars. Reasons for that are below:

(1) Especially the first week felt very slow and repetitive. Most of the material could have been summarized a much smaller timeframe.

(2) The course went over some interesting topics in a very high-level way, but skipped a lot of the details that would have been very interesting to people looking to learn deep learning in depth (like the target audience of this course!).

(3) While I think the approach of having some themed case studies for the test is neat, a lot of the answers left me thinking "well, the correct answer would also depend on X which isn't specified". Good concept to test knowledge in a "discussion/oral exam" session, but IMHO bad for hard "wrong or right" multiple choice tests.

(4) Some videos had "black screen" times at the end, errors, cut-offs and repetitions were not cut out, and overall I think this had the least amount of "polishing" of the courses in this specialization so far.

I'd have preferred if the content of this course were a bit more steamlined and merged it into the other courses of this specialization.