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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: 47,335
Рецензии: 5,432

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.

JB
1 июля 2020 г.

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!).

Фильтр по:

5151–5175 из 5,393 отзывов о курсе Structuring Machine Learning Projects

автор: John J

18 апр. 2021 г.

Appears to be some errors in the section titles (Flight Simulator??). Also, some parts didn't seem to be as polished as the previous two courses.

автор: Hernan J

2 нояб. 2020 г.

It's easy and more simple than the others in specialization. Can be more deeper into ML project organization management. It's ok, could be better!

автор: Jordon B

31 янв. 2018 г.

This course did not contain programming assignments, only quizzes, and was thus considerably less useful, even though the knowledge was important.

автор: ccbttn

12 окт. 2017 г.

Quite some questions are confusing and some are not correct itself. and this course is more concept based, didn't actually get to program a lot.

автор: Giacomo A

28 янв. 2018 г.

Contains some useful tips, but they are a bit too diluted - I feel like it could have lasted much less and still conveyed the same information.

автор: Yancey S

25 сент. 2018 г.

This course provides some interesting insights into how to approach machine learning projects, but feels a little light on substance at times.

автор: Even G

20 окт. 2017 г.

Great content. Some strange audio that I think should've been cut (especially in week 2). I suspect the week 2 quiz is a little buggy as well.

автор: Mayur S

25 мая 2020 г.

The course material can be clubbed with existing courses. It would have been much more meaningful with some examples and hands-on assignments

автор: Rindra R

11 окт. 2017 г.

Covered important topics and real-world project considerations. However, the content and assignments are too short to make it a full course.

автор: Daniel K

25 июня 2020 г.

This time it was not that well-structured than the previous courses. I thought we would learn how to structure step by step an ML project.

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: Raghu t D

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.