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

4.8
41,981 个评分
4,706 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 02, 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 31, 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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3676 - Structuring Machine Learning Projects 的 3700 个评论(共 4,659 个)

创建者 Sonal G

Sep 28, 2019

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创建者 Ishmael M

Jul 15, 2019

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创建者 Caoliangjie

Feb 20, 2019

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创建者 Dayvid V R d O

Oct 31, 2018

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创建者 Michele C

Jul 25, 2018

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创建者 Huifang L

May 08, 2018

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创建者 Yujie C

Feb 01, 2018

创建者 Jampana b

Oct 18, 2017

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创建者 StudyExchange

Aug 21, 2017

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创建者 Ali K

Mar 29, 2020

In this course, the instructor from his experience gained through several machine learning and deep learning projects explains how to prioritize tasks in a big machine learning projects. This course does not introduce the reader to CNN or RNN but rather makes the user aware of some ML/DL tips to make the most efficient use of time and resources. Some of the most important questions addressed in this course are: 1) Why a single evaluation metric is important and what are some of the widely used metrics? 2) What is human-level performance and is it a good estimate of Bayes error? 3) What is Orthogonalization in the context of ML tasks and why is it important? 4) How to measure avoidable bias, variance error, data mismatch etc? 5) How to address data mismatch error? What is transfer learning and how is it different from multi-tasking 6) Whether one should opt for traditional or end-to-end deep learning approach?

创建者 Zhenwei Z

Apr 03, 2020

This course from setting machine learning strategies, setting goals, error analysis and data distribution, migration and multitasking learning, and depth of the end-to-end neural network training and so on about the strategy of machine learning, to strengthen the depth of the first two lessons we learn the basic knowledge have the very big help, deep understanding of the depth of these knowledge is very good for our study harder to learn knowledge, such as convolution neural network. The greatest help of this course is that it makes us understand how to solve problems encountered in the actual development process and what is the most reasonable solution through two case studies.

创建者 Oleg P

Nov 30, 2018

In this course, Andrew is giving very interesting practical insights into how to proceed in different project settings and how to speed up each iteration. Think of it as a stand-alone optimization algorithm for deep learning projects. What I'd further expect from this course are practical assignments, e.g., data acquisition and preprocessing patterns, data (image) augmentation, and transfer learning and multi-task learning (preferably building upon introduction to tensorflow in the previous course). As I already stated in the previous previews, optional assignments without grading would also do the work in motivating the students to do something on their own.

创建者 Joe Z

Jan 07, 2019

Great insights as usual for these courses. Especially useful are the strategic insights for dealing with data mismatch between train and dev/test data sets; my favorite is the idea of a "train-dev" set to separate variance from the differences in data distributions, which had never occurred to me despite it being obvious in hindsight. The "flight sim" tests were more challenging than I expected, and really helped to cement the concepts into memory. The only criticism is that some coding assignments would have been helpful to put these ideas into practice in a guided manner. Otherwise, great course as I have come to expect from Andrew.

创建者 Vignesh R

Jun 01, 2018

This course is more about explaining how to set your analysis universe(train/dev sets etc.) and where to go when u hit a road block i.e. when to concentrate on bias/variance etc.

Suggestions: Unlike other courses, no programming assignments here .. may be some programming assignments + Quiz in a case study format would have been more helpful. E.g. present a case, ask the student to write piece of code to calculate bias and other metrics, and then ask questions from the metrics derived instead of mentioning directly the values for human level error, Bayes estimate.

创建者 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!

创建者 Tobie

Oct 24, 2017

A very quick course (significantly faster to complete than the preceding two courses in the specialization) that is usefully targeted at the practical aspects of how to go about developing a neural network. Prof Ng sets out a clear and logical approach to building, diagnosing issues, and iteratively improving models. The one critique I have is that a few of the topics are repeated from things already covered in the earlier courses and the editing of the videos is not done quite as well. Still, a very worthwhile use of very little time!

创建者 Teodor C

Jun 14, 2020

Very useful tips and insights on how to approach supervised ML projects. However a more in-depth case study would be interesting, to try to answer questions like: where does easily-available input data come from ? (sure CCD cameras & other tech, but but do not forget all those little labelling hands ^^) what makes the success of hand-design features in such or such domain ? can we bootstrap ML back into tools that help with valuable hand-design ? can unsupervised learning help with cleaning up input data for ML ?

创建者 Julien B

Sep 21, 2017

The course content is very instructive and will greatly improve your performance on real world machine learning projects. Basically, this course gives you recipes to improve the performance of your model when something is wrong in your data and if you have not enough data.

Compared to the first two courses in the deep learning specialization, videos were a bit of lower quality, not completely edited and the course could have featured programming assignments, notably on transfer and multitask learning.

创建者 Dorian P

Feb 22, 2020

The course is absolutely fantastic, Andrew is a fantastic lecturer, however I could not give it 5 stars as there were parts of the videos which had not been edited well. There are parts where Andrew pauses and repeat himself again which seems intentionally done to allow the editor to appropriately remove the stumbled sections and make the talking seem continuous, but editing these sections has been overlooked. Its a shame as its a slight but noticeable issue in an otherwise flawless course.

创建者 Gilles D

Sep 06, 2017

The course content is very interesting and opened my eyes of different strategies to improve the results of a Machine Learning project.

The recommendations also helped me greatly to dispel some of the myths and bluffs that run rampant in this developing field.

It makes me a better engineer. On the minus side, the course is not as polished as the two previous ones and some edit could help to cut in the content that is repeated.

Other than that, some great ideas and I am glad I took it

创建者 Anton D

Oct 26, 2017

I liked how the course gives such insights that would help in progressing efficiently with a DL project in hand. This is the kind of thing that one needs to know about in addition to all the technical aspects.

I think the course would benefit from even more examples where a concrete project is examined and the student could see how the team was progressing: what were the iterations, what challenges were resolved, what were the intermediate results and of course: the final result.

创建者 Joshua H

May 30, 2020

The course gave an extremely wholistic insight into what applying deep learning theory may be like in a commercial context. It felt as if Andrew left no stone unturned, answering every question a student could have either in the video, or in the weekly quizzes. The only adjustment I'd have liked to see is Andrew spending more time elaborating on multi-task learning networks (such as how to initialize back propagation along a network which uses multi-task learning).

创建者 Andreea A

Feb 16, 2019

Liking this course is subjective. It is indeed based on the experience of others, but since experience can't always be generalized and transferred, the lectures are repetitive and bland (they are also badly edited in Week 2). On the other hand, the two "ML flight simulators" are really interesting and answering them is not obvious. It requires a lot of thinking and focus to choose correctly from apparently equivalent solutions, which might happen in real projects.

创建者 Vrajesh I

Jun 04, 2018

Course was very theoretical as compared to the previous 2 courses in this specialization, and maybe a programming assignment could have been included (optional) where in the student could maybe learn how to play around with distributing the train/dev/test data and calculating the errors. Personally since I like hands on stuff more, these are my two cents on what could have been better :) amazing work by the teaching team and others on the backend as always!! :D

创建者 Robin S

Jun 12, 2018

Once again, Andrew bringin' the heat!However, I docked a star for a couple of reasons. First off, I feel like there could be a bit more material here, perhaps an example notebook with noising and illustrating avoidable bias / variance / data mismatch.Most importantly though, I strongly, strongly recommend you go through the Week 2 Quiz (Autonomous Driving) and double check it for spelling/typing errors. There are quite a few of them!