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

4.8
45,667 个评分
5,207 条评论

课程概述

You will learn how to build a successful machine learning project. If you aspire to be a technical leader in AI, and know how to set direction for your team's work, this course will show you how. Much of this content has never been taught elsewhere, and is drawn from my experience building and shipping many deep learning products. This course also has two "flight simulators" that let you practice decision-making as a machine learning project leader. This provides "industry experience" that you might otherwise get only after years of ML work experience. After 2 weeks, you will: - Understand how to diagnose errors in a machine learning system, and - Be able to prioritize the most promising directions for reducing error - Understand complex ML settings, such as mismatched training/test sets, and comparing to and/or surpassing human-level performance - Know how to apply end-to-end learning, transfer learning, and multi-task learning I've seen teams waste months or years through not understanding the principles taught in this course. I hope this two week course will save you months of time. This is a standalone course, and you can take this so long as you have basic machine learning knowledge. This is the third course in the Deep Learning Specialization....

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!).

AM
Nov 22, 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.

筛选依据:

5051 - Structuring Machine Learning Projects 的 5075 个评论(共 5,151 个)

创建者 Mathew S

Dec 31, 2017

Its informational

创建者 Kenneth C V

Oct 7, 2020

Complex Material

创建者 Siwei Y

Nov 28, 2017

就两周的课, 我不知道算是凑数吗

创建者 Mohit S

Jul 15, 2020

Not that good.

创建者 Fotsing B K

Feb 25, 2018

to theoritcal

创建者 Yide Z

Dec 17, 2017

too much bugs

创建者 David B

Aug 19, 2019

No Homework!

创建者 Sean L

Oct 6, 2019

Bit tedious

创建者 Leticia L R

Aug 11, 2018

Bit boring.

创建者 Wouter M

Jun 13, 2018

A bit short

创建者 Zhen T

Dec 19, 2019

Too simple

创建者 Gonzalo A M

Jan 16, 2018

Too short.

创建者 Sunil S

May 26, 2020

Knowledge

创建者 My I

Mar 15, 2019

too easy

创建者 Артеменко Е В

Sep 3, 2017

Too easy

创建者 vamshi

Aug 28, 2020

useful

创建者 Jalis M C

Jan 7, 2021

good

创建者 Debasish D

May 15, 2020

Good

创建者 Sajal J

Oct 29, 2019

okay

创建者 KimSangsoo

Sep 17, 2018

괜찮음

创建者 Benedict B

Jul 27, 2018

ich

创建者 Shawn P

Jun 8, 2018

k

创建者 Daniel S

Mar 19, 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.

创建者 Gil F

Nov 17, 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.

创建者 Alexander D

Apr 16, 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.