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

4.8
звезд
Оценки: 47,516
Рецензии: 5,450

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

AM
22 нояб. 2017 г.

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.

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.

Фильтр по:

5126–5150 из 5,412 отзывов о курсе Structuring Machine Learning Projects

автор: Abhishek S

11 мая 2020 г.

I think that a lot of this knowledge would have been useful had it been given after building a few projects ourselves (i.e - sample projects), I could not feel connected with the content much and was a little uninteresting for me.

автор: SHUBHAM G

22 июня 2018 г.

The course must have had some coding exercises showing how wrong the error analysis doesn't work and also some exercises on transfer learning, multi-task learning in order to see in practice how these concepts work in real life.

автор: Mats B

30 мар. 2019 г.

This course did not really feel like a course, just videos and ambiguous quizzes. Some repetition and poor editing of the videos. I recommend to reformat this course to be more substantial and to include programming exercises.

автор: Мар'ян Л

2 июня 2018 г.

Compare to other courses of the specialization, this has lower quality of video lectures, often repeats things from previous courses and I think it would be better to separate whole course as a separate week of a previous one.

автор: Gianfrancesco A

23 окт. 2017 г.

Very interesting course about guidelines about how to set up a project target oriented, not so trivial. Perhaps an improvement could be to add a chapter on the various DN architectures available for the various tasks.

автор: Lukas O

10 дек. 2017 г.

Would be much better if it included a programming assignment as a final project. I'd like to have a little less scaffolding during the decision-making process to see how well I can do on even more realistic problems.

автор: Gabriel S M

22 окт. 2017 г.

It is a good course because it highlights practical aspects of implementing ML. Some of the test questions were a bit ambiguous though.

I'd also like to have seen Transfer/Multi-task learning implementation exercises.

автор: Noga M

21 июля 2020 г.

I understand why this course is important, but for me it was the least favorite course so far. Some of the videos were too long and repeat themselves. Maybe it's because I have knowledge in machine learning already.

автор: Tinsae G A

12 февр. 2018 г.

This course is full of intuitions that are very difficult to remember at once. The quiz is very hard and mind teasing. For better confidence, I would like if you add one more case study.

In general the course is good

автор: Bjorn E

9 сент. 2019 г.

Interesting and practical information, but it felt stretched out in an attempt to create a two-week course. With some editing and less repeated information this could be one week that would fit in the prior course.

автор: RB

31 янв. 2018 г.

Good course to learn about structuring the projects and carrying out error analysis. I wish there were some assignment to work on in addition to the case study quizzes. Assignment really help us learn effectively

автор: Francisco S

25 окт. 2017 г.

The course was just a bunch of tips and suggestions. Yes, they are useful, but given the empirical nature of machine learning I would expect those tips to be accompanied by practical applications and homework.

автор: Amit P

21 авг. 2017 г.

I expected more. The videos were a little long and repetitive. The content was important, though. Maybe the course materials could be squeezed into one week and combined with the previous deep learning course.

автор: Viswajith K N

24 июня 2018 г.

THe course was challenging and had valuable inputs. But it would be even more wonderful if we got to work on some portion of the case studies as a capstone project at the very least. Else Its a 5 star course.

автор: daniele r

15 июля 2019 г.

Good for the numerous hints about practical issues such as different distributions on train/dev/set. Very bad for the lack of hands-on assignments. Good practical advices but no occasion to see them working!

автор: John O

16 дек. 2017 г.

The quality of the course is not up to par with the other courses in the specialization. There is very little content and it is gone through too slowly. There are also more bugs and errors in the exercises.

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.