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學生對 deeplearning.ai 提供的 Structuring Machine Learning Projects 的評價和反饋

4.8
48,875 個評分

課程概述

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....

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JB

2020年7月1日

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!).

AM

2017年11月22日

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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5476 - Structuring Machine Learning Projects 的 5500 個評論(共 5,575 個)

創建者 דוד ב

2019年8月19日

No Homework!

創建者 Sean L

2019年10月6日

Bit tedious

創建者 Leticia R

2018年8月11日

Bit boring.

創建者 Wouter M

2018年6月13日

A bit short

創建者 Zhen T

2019年12月19日

Too simple

創建者 Gonzalo A M

2018年1月16日

Too short.

創建者 Sunil S

2020年5月26日

Knowledge

創建者 My I

2019年3月15日

too easy

創建者 Артеменко Е В

2017年9月3日

Too easy

創建者 vamshi

2020年8月28日

useful

創建者 Jalis M C

2021年1月7日

good

創建者 Debasish D

2020年5月15日

Good

創建者 Sajal J

2019年10月29日

okay

創建者 KimSangsoo

2018年9月17日

괜찮음

創建者 Benedict B

2018年7月27日

ich

創建者 Shawn P

2018年6月8日

k

創建者 Daniel S

2018年3月19日

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.

創建者 Gil F

2019年11月17日

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.

創建者 Alexander D

2020年4月16日

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.

創建者 ananth s

2018年10月1日

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.

創建者 Younes A

2017年12月7日

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.

創建者 Gonzalo G A E

2020年5月12日

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.)

創建者 Maxime

2020年9月9日

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.

創建者 Miguel A M

2020年10月23日

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.

創建者 Guilherme Z

2019年9月4日

The most exciting part of the course as others in the series is the interviews that Andrew does with deep learning researchers. I thought I would learn more about how to structure actual machine learning projects from a software perspective and how I would incorporate them to real products. I felt the videos for this course were too long and cover somehow basic common sense.