Chevron Left
Zurück zu Structuring Machine Learning Projects

Bewertung und Feedback des Lernenden für Structuring Machine Learning Projects von deeplearning.ai

4.8
Sterne
48,224 Bewertungen
5,531 Bewertungen

Über den Kurs

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....

Top-Bewertungen

AM

22. Nov. 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.

TG

1. Dez. 2020

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.

Filtern nach:

5401 - 5425 von 5,498 Bewertungen für Structuring Machine Learning Projects

von Yide Z

17. Dez. 2017

too much bugs

von דוד ב

19. Aug. 2019

No Homework!

von Sean L

6. Okt. 2019

Bit tedious

von Leticia R

11. Aug. 2018

Bit boring.

von Wouter M

13. Juni 2018

A bit short

von Zhen T

19. Dez. 2019

Too simple

von Gonzalo A M

16. Jan. 2018

Too short.

von Sunil S

26. Mai 2020

Knowledge

von My I

15. März 2019

too easy

von Артеменко Е В

3. Sep. 2017

Too easy

von vamshi

28. Aug. 2020

useful

von Jalis M C

7. Jan. 2021

good

von Debasish D

15. Mai 2020

Good

von Sajal J

29. Okt. 2019

okay

von KimSangsoo

17. Sep. 2018

괜찮음

von Benedict B

27. Juli 2018

ich

von Shawn P

8. Juni 2018

k

von Daniel S

19. März 2018

Definitely not worth paying for (and I literally completed this in one afternoon). Thankfully I did not pay, so it was not that bad value in fairness.

In honesty the lack of value from this course actually says a lot about Andrew Ng's original Machine Learning course, which was consistently excellent. Actually coding in Octave for that class cemented a lot of concepts as well, which this course does not.

The title of the course suggests this is pitched towards more advanced students who already know about Machine Learning but maybe not so much about best practices. This feels far too basic for that demographic. The practices are sensible though and useful, if maybe overly focussed on massive datasets as opposed to the ones that Google *doesn't* deal with on a daily basis. Things like SMOTE could have been mentioned as well, for example.

TL;DR: This feels like a missed opportunity. My advice is don't take it if you've done Andrew Ng's ML course. Google things after that and wait for a decent course that's pitched towards intermediate students.

von Gil F

17. Nov. 2019

Notwithstanding the great video lectures this course's assignments were poorly composed:

Firstly, there are no programming assignments! I understand the material here is mostly conceptual, however subjects such as 'Transfer learning' and 'Multi - task learning' should be given as a programming assignments. In 'Transfer learning' you need to modify an existing model, which I think is a good tool for a student. Hopefully we will use it in future lessons. Lastly some of the questions in both 'quizzes' have many complaints in the forum and the same complaints reappear yearly, therefor it's a bit annoying no measures are taken to modify the questions so they will be clearer.

von Alexander D

16. Apr. 2020

This course was pretty poor. Too many of the lectures are repetitive, and the examples given to discuss the concepts seem overly simplistic. It would be far better if AN actually discussed previous cases and what pitfalls to watch out for. For example, it's useful for practitioners to understand human component features that he mentions. He's probably seen a lot of instances in which engineers came up with great ideas that ended up differentiating a mediocre-performing algorithm from a far better one. He could also discuss go into greater case study detail of instances in which transfer learning/muti-task learning worked well or not.

von ananth s

1. Okt. 2018

Very verbose with hand-wayy examples. The 18 minute lecture was the hardest Ive tried to not fall asleep. The second quiz has extremely badly written questions with multiple choice answers. Very ambiguously worded QnA. Don't mistake this review for the whole DL specialization though. Andrew's DL specialization course is brilliantly structured and an excellent primer for folks such as myself just getting into DL. It is only this section on structuring ML projects which is a little bit of a drab.

von Younes A

7. Dez. 2017

The material is great, but the production quality is so poor that I had to give 4 stars only. Videos have blank and repeating segments, and more quizes have mistakes that make getting a 100% because you know the material impossible (you have to tolerate some wrong answers to do it). This means you can't rely on quizes at all, because maybe the ones you got right were actually wrong :). The ones I got wrong were also called out by other people on the forums, so I guess maybe I am right.

von Gonzalo G A E

12. Mai 2020

This course is just a set of (perhaps useful) advice on how to make decisions when working on a project, not a course on techniques or how to actually do things. There are no programming assignments as in the other courses of the specialization, just some "decision making simulators". I learned more and enjoyed more the other courses. It feels like all these advice could be given as part of the other courses. (But perhaps I am much more technically inclined.)

von Maxime

9. Sep. 2020

This part did not interest me much because I find that it does not go into detail and concretely I did not learn anything useful. Indeed we have plenty of examples that teach us what to face in a situation but in the end if we are a beginner we simply do not know how to do ... I find that it is + a documentary that Classes.

I am hard on my scoring of this 3rd part but I strongly recommend to follow the first 2 parts which go into detail.

von Miguel A M

23. Okt. 2020

Although the content may be useful for Deep Learning researches/practitioners. I think there is no need to have a stand-alone course but rather include these guidelines or best practices in the first two courses of this specialization. Some of the concepts are as well repeated. There are no programming assignments or any other way to 'visualize'/'practice' the ideas mentioned here.