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Вернуться к Structuring Machine Learning Projects

Отзывы учащихся о курсе Structuring Machine Learning Projects от партнера deeplearning.ai

4.8
звезд
Оценки: 47,496
Рецензии: 5,445

О курсе

In the third course of the Deep Learning Specialization, you will learn how to build a successful machine learning project and get to practice decision-making as a machine learning project leader. By the end, you will be able to diagnose errors in a machine learning system; prioritize strategies for reducing errors; understand complex ML settings, such as mismatched training/test sets, and comparing to and/or surpassing human-level performance; and apply end-to-end learning, transfer learning, and multi-task learning. This is also a standalone course for learners who have basic machine learning knowledge. This course draws on Andrew Ng’s experience building and shipping many deep learning products. If you aspire to become a technical leader who can set the direction for an AI team, this course provides the "industry experience" that you might otherwise get only after years of ML work experience. The Deep Learning Specialization is our foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology. It provides a pathway for you to gain the knowledge and skills to apply machine learning to your work, level up your technical career, and take the definitive step in the world of AI....

Лучшие рецензии

TG
1 дек. 2020 г.

I learned so many things in this module. I learned that how to do error analysis and different kind of the learning techniques. Thanks Professor Andrew Ng to provide such a valuable and updated stuff.

MG
30 мар. 2020 г.

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.

Фильтр по:

5176–5200 из 5,407 отзывов о курсе Structuring Machine Learning Projects

автор: José G

18 апр. 2020 г.

Lots of information, few knowledge

Change name to "Struc. Deep Learning Projects", all other forms of ML not considered, specially for P2.

автор: Eric K

21 июля 2018 г.

Too much similar material to the prior course, and only two simple quizzes, no hands-on programming assignments like in earlier courses.

автор: Eric M

20 окт. 2017 г.

A fundamentally very good course with a few technical gltiches that can be easily corrected and some confusing elements to be clarified.

автор: Bongsang K

21 мая 2018 г.

I think this lecture is important for every research scientist. However, there was no programming examples so I was confused sometimes.

автор: Michael L

1 мая 2018 г.

No programming assignments or labs, so too much theory, and too little chance to put same into practice. Not a good value for my money.

автор: Max S

13 дек. 2017 г.

Still good but getting much sloppier. Bad editing of the videos, some exercises plain wrong and staff not reacting to forum posts, etc.

автор: Xiang L

26 апр. 2021 г.

This session might not be very helpful for people from different backgrounds such as non-industral level application of deep learning.

автор: Lars L

30 дек. 2017 г.

Course materials need some cleanup. Were a number of audio blips, in the video. Material was good but just didn't seem as polished.

автор: Nitin S

25 июня 2020 г.

Decent learning. Though quite some stuff, I felt as repetitive and obvious.

I wish there was some programming exposure as well here

автор: Taavi K

29 нояб. 2017 г.

Too short on its own (took half a day to go through the whole thing), could have been combined with Course 2 of the specialization.

автор: Jean-Michel P

29 июня 2021 г.

I feel like this course should be broken down and included in the other courses to get better context within these other courses.

автор: sai r t

6 авг. 2018 г.

this session was good it would be more better if they provided the code of them..so that we could be abke to learn more from them

автор: Denys G

23 нояб. 2017 г.

Felt a bit rushed, each video was full of good tips but personally I think each video should have been a jupyternotebook instead.

автор: Massimo A

18 нояб. 2017 г.

More theoretical than the other courses in the specialisation but still very high quality.

Short but with a lot of information.

автор: David P

17 окт. 2017 г.

Not nearly as good as the first two courses. These two weeks should probably be added into the second course at some point...

автор: Oliver O

16 окт. 2017 г.

Would like more applied discussion and for it to be Longer. In particular I would like to see a discussion on class imbalance.

автор: Shuai W

19 сент. 2017 г.

The content of this course is a bit too little for me.

However, it provides useful guidance for my projects. Much appreciated!

автор: Gary S

15 сент. 2017 г.

Not nearly as valuable as the first Deep Learning course. And the questions posed in the quizzes seemed far more subjective.

автор: Pejman M

21 окт. 2017 г.

Programming practices with TensorFlow should have continued in this course. Unfortunately, these two weeks were all talking.

автор: Nithin V

3 янв. 2021 г.

Need more quizzes, assignments to deepen the understanding, But otherwise thank you Andrew Ng for presenting this material

автор: Panos K

18 апр. 2021 г.

The pace of the first part of the course was too slow. The second part (from Transfer learning onwards) was much better.

автор: Mustafa H

16 июля 2018 г.

This course does discuss interesting and important subjects but I feel it can be combined with course 2 of this series

автор: Ahmed A

10 июля 2018 г.

course is very good have a lot of important theory, it will be amazing if become 3 weeks with programming assignments.

автор: Kevin Q

19 мар. 2018 г.

lot of issues with assignments and ambiguous quiz questions this time around, not as polished as other Andrew courses

автор: Arghya R

19 сент. 2017 г.

Could have more case studies and above all. Also programing assignments on self driving car could have been better