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Comentários e feedback de alunos de Structuring Machine Learning Projects da instituição deeplearning.ai

4.8
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38,941 classificações
4,271 avaliações

Sobre o curso

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....

Melhores avaliações

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.

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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3326 — 3350 de {totalReviews} Avaliações para o Structuring Machine Learning Projects

por 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.

por 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.

por 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.

por David M C

Jul 24, 2019

There were many video editing errors and grammatical errors in the writing in this course. I felt an increased cognitive load and more frequent distractions because of this. I would be happy to volunteer some time to help with this if you contact me.

The pacing of this course felt strange; week 1 was very short and the material felt less important that week 2.

I would have really enjoyed a programming assignment with transfer learning. Since we already built a cat identifier, making a monkey identifier, or flower identifier would have been lovely and fairly easy, I think.

por 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.

por 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!

por 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!

por 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.

por 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.

por 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

por 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.

por 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).

por 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.

por 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

por 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!

por Rameses

Nov 15, 2019

Great practical advice on actually structuring and implementing machine learning projects. However the case study approach is more useful for people already in the field and working on projects than for some of us who are not yet in the field but attempting to gain exposure and knowledge in Machine Learning. I guess the value of these case studies will be more apparent when I actually start implementing ML projects in the real world

por Sebastian H

Apr 26, 2018

I find this course very relevant for practitioners. Perhaps from a team/organizational point of view it is the most relevant course. I agree that the concepts presented essentially distinguish the great from the average developer team. However, some of the material is very practical and I feel that right way to learn it is by doing it. To be fair it is very difficult to reflect that in a course! Overall I think it is very useful.

por P M K

Nov 30, 2017

Hi, This course though very useful had become a bit monotonous and at times a bit difficult to understand. There could have been better presentation giving more examples. The Quiz had really tough questions , in some cases the language is not clear. I request the course mentors to look into the same. Nevertheless, it has definitely been very useful as it highlights the practical problems faced and ways to resolve them

por Shuai X

Dec 15, 2017

This Course offers simple, useful and general tips for starting a typical deep learning project. The most valuable part is on how to split datasets and how to identify possible data distribution mismatch. The tips and case studies do not always work in real application. But that is perhaps because the course is intending to be simple. This course does not require any math backgrounds and can be completed in 4 hours.

por Harry ( D

Aug 12, 2018

Although I see other learners saying that this is the worse of all the Deep Learning specialization courses because there are no programming assignments, I believe it was a very useful course full of practical knowledge for properly structuring ML projects. I agree, however, that video quality is worse that the other courses and there are some editing issues (some video segments repeat, blank sections, etc.)

por David R R

Nov 17, 2017

This course is hard to complete because the lessons are very large and difficult to understand. However, I recommend this course for anyone than want to apply deep learning in real enterprise world.

Este curso es dificil de completar ya que las lecciones son muy largas y costosas de entender. Sin embargo, recomiendo el curso para todo aquel que quiera aplicar deep learning en el mundo empresarial.

por Jason T

May 27, 2018

I liked this course a lot, since it introduces transfer learning and multi-task learning and so moves you toward more powerful and realistic AI applications than the previous courses in the specialization. However, I missed the programming assignments that aided understanding so much in the previous courses. The quiz by itself was not as effective at illustrating the key concepts.

por Matt E

Apr 09, 2018

I wouldn't really consider this a "course," but the stuff he taught was great. However, Andrew could go much deeper into these topics. Some real data examples that he has come across would be even more helpful. Seeing how he codes his approaches in python would also be a very useful (and quick) batch of lectures. If he needed to extend it another week that would be understandable.

por Stoyan S

Oct 01, 2017

Excellent course just like the previous two. Short programming exercises would have been nice to have. Some of the answers in the quiz were too similar and this might be quite confusing for non-native English speakers and therefore can reflect more knowledge in English language rather than knowledge in related machine learning topics. I am looking forward for the next 2 courses.

por Mikko H

Sep 24, 2017

Great material that's clearly based on valuable practical experience. I and found the "machine learning flight simulator" quizzes to be an educational format. However, the editing of the quiz questions (grammar, matching question types with wording in the question etc) was not flawless in September 2017. This course would benefit from another review pass from this perspective.