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

4.8
звезд
Оценки: 47,353
Рецензии: 5,433

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

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!).

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.

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5126–5150 из 5,396 отзывов о курсе Structuring Machine Learning Projects

автор: 臧雷

5 сент. 2017 г.

Most of the materials in this course is tedious and have already been taught in previous courses. But I suggest the Transfer Learning and Multi-task Learning part, as well as the end-to-end learning part.

автор: Wells J

16 дек. 2017 г.

The course was misleading on what homework there was (machine learning flight simulation?) There was no homework. and the lectures were pretty bland compared to other courses in this specialty.

автор: Karthik R

4 мар. 2018 г.

Transfer Learning and Multi-Task learning discussed in the course would greatly benefit from having programming assignments where people can play around with the data and learn confidently.

автор: Andrew W

5 авг. 2019 г.

Good information about how to structure projects and how to boost performance. Not very hands-on however. Fits in well with the Specialization though as a break before CNN's and sequences.

автор: Daniel C

19 нояб. 2017 г.

Not as helpful... just a few suggestions and ideas... but there's no great application of the information learned here like a walk-through project or something with code, that's graded.

автор: Luiz C

22 окт. 2017 г.

less useful than previous courses.

Would appreciate to go much deeper in directions like CNN, RNN, RL and review Unsupervised Learning (which was too light, ... no mention about RBM)

автор: Akshay S

18 июня 2020 г.

It was very theoretical and subjective.

It would be useful if the learner has some more experience in DNN than currently expected.

But I definitely enjoyed 2nd week of the course.

автор: Andrew C

29 окт. 2017 г.

Interesting content, but lacking in real work. As with all of the deeplearning.ai courses thus far, the multiple choice questions are frequently ambiguous and poorly worded.

автор: Zsolt K

24 сент. 2018 г.

The information is really basic, most of it is self explanatory. This shouldn't be a course on its own, rather maybe a week/half weeks worth of material in another course.

автор: Sherif A

25 нояб. 2017 г.

This course is too subjective. Andrew shares his experience in a structured way in the lecture. However, I feel that correct structuring decisions need to be brainstormed.

автор: Patrick F

12 дек. 2019 г.

Seeing different practical use scenarios and adaptions is fine but it got pretty boring without a real application to tune. The Quizzes on the other hand were very good!

автор: Alberto S

29 мая 2018 г.

Although everything taught is relevant, it was too much theoretical. And some of the evaluation questions are not clear (well, at least for non native English speakers).

автор: Daniel V

26 февр. 2020 г.

Generally useful skills, but the contents partially overlap with previous courses and the overall quality doesn't match the previous courses (eg poor video mastering).

автор: Davide C

26 нояб. 2017 г.

The course was interesting, but in my opinion too theoretical. I preferred the first 2 courses with Python programming. I am now looking forward to the next 2 courses.

автор: Felipe L d S

7 июня 2018 г.

Even though some of the content is useful, I feel like this course should be merged with the second one. There is not new information enough to justify a new course.

автор: Thomas J

24 янв. 2018 г.

Good material was presented in this course but there were a number of technical errors in the video recordings. If they were cleaned up this course would be perfect.

автор: Jose P

30 сент. 2019 г.

Topics are a bit vague, which is fine as the content is interesting and useful nonetheless, but perhaps exposition is too lengthy relative to the amount of content.

автор: Robbin R

17 февр. 2018 г.

Gives good insights on how to work on a Machine Learning project yet. Provides some rule of thumbs for different hick-ups that may be encountered during a project.

автор: Nick S

8 сент. 2017 г.

even though there are great tips and advices, it does not justify an entire course and they can be mentioned in 3 videos so a lot of the videos were repetitive.

автор: Kan X

18 февр. 2020 г.

I like this specialization in general. However, this third one has too many overlapping contents and some videos are not that useful. Just personal opinion.

автор: Jkernec

23 дек. 2017 г.

Homework is lacking. It is too easy to pass. I feel like the programming task or homework task fell short. The lectures were good but too little practice.

автор: Hanbo L

22 сент. 2019 г.

Good non-technical materials, but short enough to be incorporated into other courses. Some aspects feel subjective. Many typos/minor mistakes in quizzes

автор: vincent p

24 авг. 2019 г.

Was really enthousiastic about the first two courses in the specialization, the third however felt a bit like going back a step in level of advancement.

автор: Rishabh G

22 мая 2020 г.

A different course for only two weeks of content? This is nuts. I waited for 15 days for financial aid to be approved and I completed it within 5 days.

автор: Leitner C S E S

15 сент. 2017 г.

Only interesting if you don't have much experience with machine learning; Might or might not be great if you are a novice, though - hard to say for me.