Chevron Left
Voltar para Structuring Machine Learning Projects

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

4.8
estrelas
47,351 classificações
5,433 avaliações

Sobre o curso

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....

Melhores avaliações

TG
1 de Dez de 2020

I learned so many things in this module. I learned that how to do error analysis and different kind of the learning techniques. Thanks Professor Andrew Ng to provide such a valuable and updated stuff.

JB
1 de Jul de 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!).

Filtrar por:

4426 — 4450 de 5,395 Avaliações para o Structuring Machine Learning Projects

por 张之晗(ZhiHan Z

29 de Ago de 2017

Comparing other courses before, it focus more on structing deep learning program and evaluate properly. However, the content in this week is really boring. In my opinion, it is better to imptove the course by teaching more implementation codes.

por Santiago R A

2 de Dez de 2017

Some questions in week 2 test are ambiguous and the last videos have edition errors. But overall strategies for guiding projects are very useful. It's a great course about practical aspects of Deep Learning you'll probably not find anywhere.

por Akansh M

12 de Jun de 2020

I have previously worked on DL project and its performance was not good in real-world data, I wasn't able to draw any reason for it. This course taught me how to deal with such kind of problem and how one can approach the possible solution.

por Max

17 de Jun de 2018

Was a good course with a lot of useful tips that I am sure I am able to use in my job as a data scientist. However, I would've liked if there were a few more hands-on examples (e.g. using jupyter) to really drives these concepts more home.

por Nityesh A

10 de Out de 2017

The course could have been much shorter than it is because Andrew seems to be repeating his simple ideas a lot in the lectures. However, each simple advice seems important for practical purposes (I am willing to take Andrew's word for it).

por Mikhail F

20 de Out de 2019

It might be not that trivial. But some hand-on experience with some code might be good here as well. As many practice as possible would be beneficial to the learners, coupled with great explanations from Andrew that are already in place.

por Alon S

23 de Set de 2019

I think the quizzes should be considerably longer, to include more scenarios, and also have fewer questions that rest on technicalities (where some of the answers are almost correct except they misuse a term or give a wrong description).

por Michael T

26 de Out de 2017

While the simulation is unique and very useful feature of this specialization. I believe examples with data would add to the leaning experience by allowing a student to actually run the scenarios and experience the qualitative changes.

por Mark M

21 de Nov de 2017

This course is at all an important part during the learning journey. The only reason why I not rate full 5 stars that the recommendation ramen little bit on high level and do not address typical frame conditions in real world projects.

por Oliver M

16 de Ago de 2017

Lots of practical stuff about training models. But you should try building a few models before doing the course. Otherwise, you may not fully appreciate how much time can be wasted unless you use Andrew's clear and logical approaches.

por Wei Z

22 de Out de 2017

Lots of interesting and useful idea. Unfortunately the editing is poor and Professor Andrew Ng has gone a little bit repetitive in his talking in this course only. The two previous courses were great but this one is kind of dragging.

por Saad T

6 de Set de 2017

I am a big fan of the jupyter notebook assignments. I can understand that it could be hard to build python assignments for this course, but not impossible I think (maybe around error analysis, impact of artificial data synthesis...)

por S A

11 de Jun de 2018

The content of the course lecture is great. The teaching is great. One problem is the quality of subtitles. The black background does not allow to see what is shown behind. It would be better if the background would be transparent.

por Sarah W

21 de Mar de 2018

Great material! Some of the videos went a bit long, and I think the point could have been made in much less time. However, overall this series has been great and I still got some very valuable info out of this course, so I'm happy.

por Michael A

7 de Dez de 2017

The course was very well structured and Andrews explanations was wonderful as usual. The only thing I was missing was more practical hands-on in the form of a programming exercise or two to really demonstrates the different ideas.

por Hanling S

8 de Dez de 2020

Andrew really provided great content, but the edition of this course is not as good as the first two, sometimes you will hear some repetitive sentences or a long pause. Hope they can upgrade this part, all the others are terrific.

por Cheng J

20 de Set de 2020

This course give a lot of useful practical advices on training a machine learning/deep learning models. However, some of the advices are rather subjective and experience based, and some of the homework answers are quite debatable.

por ashwin m

1 de Jul de 2019

this course provided very interesting insight into missing , incorrectly classified labels and also how existing models can influence the training of a new model which is on similar lines as the task the existing models performed

por Jithin V

3 de Jan de 2021

Great course for machine learning strategies in deep learning.

Several concepts which aren't discussed in other courses have mentioned .

Especially the new way of splitting the datasets, transfer learning, multitask learning etc.

por Silvério M P

6 de Set de 2018

Looking at practical examples is an enormous help and some concepts i learned here will undoubtedly be useful in the future, i just think there should be more of it. It's just really short both in duration as well as content

por Vignesh S

28 de Mai de 2019

It was really good to know how to structure and tune the nn so as to achieve a better model. But, I felt that it had too much theory in it that is hard to remember every time a model is to be designed. Overall, it was good.

por Rahul P

24 de Ago de 2020

One of the quick and great course for individual and team for understanding how to handle and structure the machine learning project. how to improve accuracy and handle error such a wonderful course made by deeplearning.ai

por chandrashekar r

18 de Set de 2017

I rate the course high. Unfortunately many of questions (posed in the forum) have not been answered.

Her are some suggestions:

Have quiz after every lecture. That will firm up the concepts.

Give lesser help in assignments.

por Gustavo S d S

4 de Jan de 2018

Gives a sense about improving the performance of Deep Neural Networks, with error/bias/variance/data mismatch analysis. However, there is a lack of hands-on exercises, not having a programming assignment, only quizzes.

por Michael F

19 de Out de 2018

Lots of useful tips and tricks in this course. I feel that the videos could have been a bit shorter, and it would have been nice to have some programming assignments. Overall the course was extremely useful, however.