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

4.8
27,874 个评分
2,966 个审阅

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

AM

Nov 23, 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.

WG

Mar 19, 2019

Though it might not seem imminently useful, the course notes I've referred back to the most come from this class. This course is could be summarized as a machine learning master giving useful advice.

筛选依据:

1 - Structuring Machine Learning Projects 的 25 个评论(共 2,944 个)

By Liu H

Jun 11, 2019

This course would be immensely helpful for those who have not started on their first machine learning project. However, the insights shared are quite commonsensical and intuitive for those who have already had some minimal experience in machine learning. This course also does not feel as substantial as the other courses in the specialization, though the tips provided are definitely valuable.

By ABHISHEK K

May 31, 2019

I recommend this course. This will be a bit of theoretical which is good. It will talk about real world scenarios over the errors which is what we deal in day-to-day life and how to deal with it.

By Nazarii N

May 25, 2019

more practice!

By Matei I

Feb 16, 2019

I'm glad I spent some time on the "Flight simulator" assignments in this course. It's the first time in the specialization when I actually found the quiz questions challenging, and that's a welcome change. However, I didn't learn too much from the lectures. They were too repetitive, either repeating themselves or the material from the previous course. One or two videos could also do with better editing work: I could hear Andrew making a soundcheck, and there's a 30sec segment that's played twice in a row. Overall, it's probably worth doing this course, given that it requires very little time, and the assignments are useful.

By Benoit D

Jun 16, 2019

Lack of practical assignments

By AEAM

Jun 16, 2019

This is a great course, something I will keep coming back to even after I'm done because it talks about strategy and rules of thumb re: Machine Learning/ Deep Learning approaches. It introduced me to certain concepts that were brand new for me and that was a great outcome for me. I wish the audio was better and the notes were better because writing on the small screen really hinders expressibility. I would rather have Dr. Ng write/draw on a chalk board than the small screen, I feel it really constrains his process. Still it's a great course!

By Animesh S

Jun 16, 2019

I see the point, but takes too long to make it. But part of a great series.

By Julio E H E

Jun 16, 2019

This course is very helpful to learn best-practices and problem-solving strategies that can help improve our deep learning algorithms. While I think the ultimate way of learning is through practice, here you can at least get a list of things to try in the future as you work on these algorithms.

By Dien-Lin T

Jun 16, 2019

The explanation of the concepts is very easy to understand, and the assignments are really helpful.

By Bradley D

Jun 15, 2019

There's theory, but, without practice and application in my opinion. I did not like it because it seems to be easily forgotten seeing that I did not associate with practical excercises.

By Hsinyu, C

Jun 15, 2019

a very practical course!

By SAHADEVAREDDY A S

Jun 15, 2019

Really very helpful

By Shreyas C

Jun 15, 2019

It is good course to understand real world problems.There is too much theoretical knowledge and exposure but sometimes it is boring also.

By DurgaSandeep

Jun 15, 2019

Too good and more informative course in NN

By Donato T

Jun 15, 2019

Very valuable insight in real world usage of DL and guidance in approaching DL projects

By Liam M

Jun 15, 2019

Kinda boring, but still pretty practical.

By Nigel S

Jun 14, 2019

Another really useful course on Neural Networks that will help improve your results, and it's only 2 weeks.

By Chima E

Jun 14, 2019

This course was very helpful.

By João A J d S

Jun 14, 2019

It is a useful Content to keep in mind. Not as practical as other contents in the Specialisation. But, as Andrew Ng pointed out, it is a topic many times overlooked and I'm glad it was discussed.

By VIKASH K C

Jun 14, 2019

really awesome course !!!!

By Jenhan T

Jun 13, 2019

Andrew Ng's lecture on these topics are really a public service. High quality content from an expert.

By Trương N G

Jun 12, 2019

This course is easy to understand. I have learned many new things.

By 张新会

Jun 12, 2019

x学习到了迁移学习的知识

By Jayesh R V

Jun 12, 2019

Very informative.

By Sasirekha K

Jun 12, 2019

This course is a fantastic guideline to some very real problems that I am working on in the industry. Thank you, deeplearning.ai team, for breaking down research practices into more definable steps. I already see how I can be more efficient in solving some DL problems.