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学生对 deeplearning.ai 提供的 Structuring Machine Learning Projects 的评价和反馈

4.8
46,089 个评分
5,265 条评论

课程概述

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....

热门审阅

JB
Jul 1, 2020

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
Mar 30, 2020

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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4901 - Structuring Machine Learning Projects 的 4925 个评论(共 5,209 个)

创建者 Xizewen H

Oct 27, 2017

Great materials but 1) quiz questions are sometimes vaguely stated thus causes confusion, while almost no one from the course stuff is giving satisfying answers in the forum to help clarify; 2) multiple mistakes in video editing, e.g. part of clips played repeatedly, and blank dark background without any content somehow got inserted into the video; 3) really hope to see another programming assignment in Tensorflow; not that I don't agree with pilot-training assignment, but programming would be good to have because essentially this is where data science projects are built.

创建者 Apolo T A B

Nov 11, 2019

Not exactly what the title promises. In this course you will learn more about the overall approach of a ML than how to organize your data and best practices on comunicating and sharing information. (at least in week one, so far haven't started week 2).

Now I've done week 2, is much better than week 1, but still the problems presented are way more in a way of the rational behind the ML projects than Structuring the project itself, peharps a better title would be: "DEFINING GOOD MACHINE LEARNING STRATEGY APPROACHES" or something like it.

创建者 Georg S

Dec 26, 2020

I like the project perspective on ML tasks and the content a lot. I have two critics though:

1.) I am missing at least some smaller steps into the direction of implementing certain concepts (e.g. changing a model for transfer learning purposes)

2.) In addition, the videos are quite long, sometimes it seems as if the same audio/video sequence was added to one video multiple times.

Anyways, many thanks for this course. I think with some minor improvements it will reach the level of the other courses which are simply great. Many thanks!

创建者 Rupert H

Jul 6, 2020

Whereas the 2 courses that preceded this one in the specialization are focused on explaining how Deep Neural Networks work, this course is more for people with experience of NNs and how to troubleshoot issues that might occur in the wild.

I think the content here is really great, but if you're someone like me with no real world experience of Deep Learning, it is not so interesting as the other courses which explain the core concepts of the approach, rather than how to fine tune a real system to get better performance.

创建者 Martín A B

Oct 23, 2017

The curse is quite simple, there are a few interesting insights so it's not all bad. I feel I've learnt some interesting ideas. However, I feel it's quite incomplete. There are several problems that happen "in the wild" that are not covered. There is more that image classification and speech recognition to machine learning, therefore the experience of Andrew makes the course content biased to problems that are interesting but very specific. I was expecting something better given the quality of the first ML course.

创建者 Péter T

Apr 17, 2018

While it was useful to see some of the best practices in ML, and the course contains practical information, the information could be delivered more concisely. Also, we get a lot of intuition, but the delivering of the material is getting less and less rigorous. The very least it would be nice to see some sources attached to each video. 3 stars may be a bit harsh, and it does not mean that I do not think it is important to listen to this course, it is more about the way of delivering the information.

创建者 Justin M

Dec 2, 2018

As always Dr. Andrew Ng offers great insights into specifics of hot topics (Multi-Task & Transfer Learning) as well as providing unique "studies" as quizzes to complete each week. These quizzes are the primary take-away from the 2 weeks that offer a lot of redundant lecture material. Save some time... just make the 'simulations' the focus of the class then... perhaps use some transfer learning toward a different application in the quiz.

创建者 Alan S

Oct 1, 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)

创建者 Shahin A

Mar 19, 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.

创建者 Aaron L

Nov 30, 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.

创建者 Matthieu D

May 13, 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.

创建者 Nathan W

Feb 19, 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.

创建者 Reza S

Feb 15, 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.

创建者 Jason C

Dec 26, 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.

创建者 mythorganizer

Aug 28, 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.

创建者 SAGAR B

Oct 29, 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.

创建者 Fabian A R G

Oct 28, 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.

创建者 David B

Oct 6, 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?

创建者 kritika

Mar 26, 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.

创建者 Andrej P

Jan 26, 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.

创建者 Filip R

Mar 18, 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.

创建者 Joshua O

Oct 19, 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

创建者 Kaitlin P

Dec 13, 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.

创建者 Jacob T

Nov 29, 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.

创建者 Vijay A

Dec 23, 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.