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

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48,146 Bewertungen
5,525 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

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.

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.

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5176 - 5200 von 5,491 Bewertungen für Structuring Machine Learning Projects

von Alan S

1. Okt. 2017

This is a decent course, but I found it less useful than other courses so far. There seemed like a lot of redundancy and repetitiveness in the descriptions, and I think all of the information could easily be fit into a single week that more concisely captures the exact same information. The quizzes in this course were interesting because it had a very applied nature (trying to capture real world scenarios you may encounter)

von Shahin A

19. März 2020

This is a valuable but misplaced course. After the first two courses, I expected to get hands-on experience with TF+Keras, and after that, or beside it, learn about strategies of tackling ML projects. However, by first talking about the strategies, one could miss many valuable points because one is not deeply aware of the necessity of these points. Hence, the course was boring comparing the last two.

von Aaron L

30. Nov. 2017

Good class, but I think as part of the Deep Learning specialization that it'd be more useful if there were some programming exercises to reinforce what is taught in the videos.

Week 1 seems to reference a "flight simulation" programming assignment, but then it just has a description and a "mark as completed" button. Maybe this programming assignment is still being worked on or the content is wrong.

von Matthieu D

13. Mai 2018

I'm grading this course lower than I graded the two previous ones for two reasons: 1) while there are many examples given in the course, it is actually hard to take a step back and see how to concretely achieve some goals in a more generic manner, and 2) in the assignments (which are made of quizzes), many "wrong" answers would actually be appropriate if more context was given.

von Nathan W

19. Feb. 2021

This course really felt a lot more thrown together than the other ones, with a less cohesive lesson and quizzes that had more subjective material in them than usual. And perhaps it is a bit nitpicky, but I found the swipe Ng took at computational linguists to be kinda distasteful. I know there is a lot of bad blood between ML and AI people, but it has no place in coursework.

von Reza S

15. Feb. 2020

Thanks Andrew for this course! However, it is obvious that less care was taken for the preparation of this course compared to previous courses (more typos, etc). Some of the sentences in the quiz were not clear at all and made it very confusing to choose from the options. A little programming assignment at least would be nice to reinforce our learning of the materials.

von Gustaf B

10. Mai 2021

The course goes through valuable practices when it comes to analyzing errors, and Andrew does a great job at explaining. Though, I felt that there should have been programming assignments to accompany the theory. I strongly prefer the layout of previous courses, "quiz -> programming" as that feels more interactive than just doing a quiz in the end.

von Jason C

26. Dez. 2017

nice lectures and very useful knowledge learned by Andrew, but it is really short and no working assignment through real code.... and quite a lot more mistake than course1 and 2. Really love the two previous courses, don't work why the quality of the course drop off so sharply.

Somewhat disappointed, but still really great lectures.

von mythorganizer

28. Aug. 2020

It gave much more industry driven approaches to improving the model. I as a student don't have that much experience with deeplearning and that' why I couldn't relate with most of the topics that were going on here. Of course, the teaching quality was supreme. But the course's contents itself felt a little bit dry to me.

von Sagar B

29. Okt. 2017

The course work is really good. It has a practical emphasis. However, I did not like the quizzes (especially week 2 quiz) in the sense that the options are not very clear to understand and you end up being more confused. I hope the team works on the clarity of options for people who take it in future.

von Fabian A R G

28. Okt. 2017

Even though the materials in the course are very interesting, I would expect that in the third course we would have more tools in order to work by ourselves in a project... It would have been amazing a final project where you can put together this tools. Nevertheless it is still an interesting course.

von David B

6. Okt. 2017

This course was less satisfying then the 2 previous in the specialization. A lot of repetitions, no programming exercices. Interesting test cases but feels a little out of scope because we have not done image and speech reccon yet. Consider putting the course at the end of the specialization maybe?

von kritika

26. März 2019

I think the week 1 was overstreched. There was not much content to deliver and for the first time Andrew's classes made me sleep. It was like the boring lectures we get at school. I think we can easily shorten the length of this course or just scrape it and add it to course 2.

von Andrej P

26. Jan. 2018

I found this course to be a bit confusing with regards to what data set (training/dev/test) to fix under what conditions and so on. I've also missed having a practical home work, the case studies were fine, but I find that practical applications help me remember things better.

von Filip R

18. März 2020

Some of the quiz questions (especially in the first week) were quite ambiguous. If I did not take the quiz directly after the videos, I don't believe I would be able to pass, Also some written summaries as in the 1st Ng's Machine Learning course would be helpful.

von Joshua O

19. Okt. 2018

Some helpful advice here and there, but a lot of it seemed like common sense. It was not that difficult and a tad boring. Would maybe benefit from having us do actually data collection and cleaning tasks, or implement a ML pipeline and monitoring for the pipeline

von Kj C

13. Dez. 2017

Generally provides very good advice. Perhaps this course better placed at the end of the course as there isn't much hands-on experience involved and students would benefit form having experience with CNN's and RNN's prior to thinking on project-level scales.

von Jacob T

29. Nov. 2017

Too many broad statements of "yeah, we generally do this thing for best results" with very little explanation of the background theory. I don't expect advanced math and derivations, but better intuition into why certain best practices exist would be nice.

von Vijay A

23. Dez. 2019

This course was good, but it was pretty light on content to be considered a separate course by itself. Though the content is valuable, it could've been included as additional/bonus content on either of the first two courses in the DeepLearnign.ai series.

von Tom B

13. Apr. 2018

I didn't find this course as engaging as Course 1 -- there weren't any coding exercises and it felt like a bit of a let-down after the excitement of coding in Course 1. But it may turn out to have value when trying to start a new AI project from scratch.

von Francesco B

6. Okt. 2017

This course felt a bit "padded" compared to the previous ones. Also the lack of programming exercises made it seem more theoretical. Finally, the material seems rushed, e.g. there are mistakes in the video editing, strangely long pauses by the teacher.

von Peter G

5. Dez. 2017

Many helpful insights and advice from an experienced person is always great, but I don't thing this can be qualified as a complete 'course'. As I now see it - Course 2 and 3 of this specialization could easily be merged into one without loosing much.

von Maulik S

31. Mai 2020

The course should have had at least two more quizzes to understand the content better. Also, I would suggest adding programming exercises that help to better explore the ideas of orthogonality, train-dev set correction, and data synthesis.

von Kanghoon Y

4. Sep. 2019

I got an intuitions from this lectures. But What I want to get from this lecture when I first saw the title, is the method how we can define the activation function at multi-task learning etc. In this video, I got only the overall flows.

von JATIN S

27. Aug. 2020

This course to me seemed a bit too much theoretical.This could have been a little more assignment weighted so as to bring more focus to study and practise.Overall the case studies were pretty thorough to cover the course material.