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

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47,526 classificações
5,451 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

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!).

MG
30 de Mar de 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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4426 — 4450 de 5,412 Avaliações para o Structuring Machine Learning Projects

por Boris V

21 de Jan de 2018

Great material, but it's not quite easy to understand it from scratch, if you didn't have such problems yourself (i.e if you have no experience in deep NN training). I've stored this material and going to revisit it after I gain more experience in training NNs.

por Fredrik K

6 de Out de 2017

Great course, however the quiz of week 2 had some ambigious phrasings and I think at least one example (the one with the data synthesis of foggy images) is contradictive of what was taught in the video lessons. Other than that, really good content and teaching!

por Bharath S

20 de Abr de 2019

A lot of concepts were put forward and taught well. If there was a programming assignment as well to back up the concepts that were taught like multi-task learning, how to deal with data mismatch, dividing the total data into train\train-dev\dev\test data etc.

por Sanskar A

22 de Mar de 2020

I feel there is a glitch because even after completing the videos, it is not shown as completed and I had to replay them multiple times. Also there is a glitch in the assignment, because the correct answer in one attempt is shown as incorrect in the next try

por Eemeli L

19 de Nov de 2019

Great and easy-to-follow introduction to structuring machine learning projects and focusing on what to tune on neural networks. One star left out because the content has not been polished, but there are minor errors here and there with separate corrections.

por Irene Z

8 de Jun de 2019

The course seems a little less concrete than the others in this specialisation. But nevertheless, still a useful building block in anyone's deep learning repertoire. And note it will probably take less time to complete than the others, so plan accordingly.

por sakares s

24 de Ago de 2017

It would be nice if there are hands on assignment or small projects on fine-tuning with existing weight you can found in the internet or multi-task learning project. Overall, it's a great course with many useful technique to try in the real world projects.

por Alejandro J C O

16 de Fev de 2020

The course was really great, but a little part of the content was repeated from previous courses of the specialization. Also there should be more quizzes or exercises to master the large amount of practical advices for managing machine learning projects.

por Han T L

24 de Mar de 2021

Very good class! It really hits me that AI programming is a different paradigm. Managing data is key.

That said, the materials in week1 have quite a bit of overlap with the 1st course (NN & DL). The materials should simply do a quick reminder a move on.

por Paul H

8 de Dez de 2017

I liked this course, but not as much as the others. It is however setting the foundation for the remainder of the course material. It carries with it wisdom, which I think will make more sense at a later point when confronted with real life challenges

por Clint S

14 de Mar de 2020

This is the course that really confirms Andrew Ng's grasp on the practically application of AI and ML. As long as you pay attention to what is said, you will get a lot from this course. I wish there was an edited collection of notes for this course

por Lars O A

29 de Mai de 2018

Very useful part of the course set. Would like it to be slightly longer with more examples of TensorFlow and Keras. I felt that I put to much effort of trying to understand Keras in course number 5 instead of learning the principles and algorithms.

por John S

19 de Mai de 2019

I like the "flight simulator" quizzes a lot and other courses might benefit from a similar assessment (in addition to regular quizzes and programming exercises), but I do think this course would benefit from some programming exercises too. Thanks!

por Mateo A

18 de Nov de 2020

It is a great course! thanks everyone involved in making it! If you can make more questions in every video or so i think it would be better. Also different case scenarios like the one you presented here in order for students to generalize better

por Scott B

12 de Nov de 2019

I enjoyed the course content a lot, but noticed a lot of errors in test materials and test sections that didn't seem to make sense. For example, there were references to a flight simulator in quizzes that actually never appeared in any questions

por Novin S

3 de Mar de 2018

I wish it could have had some coding practices and more mathematical insights. For instance, more insights on the different metrics (precision, recall, f-measure) and having some coding practices to get better sense of it on real world examples!

por Bo S

26 de Dez de 2017

Very useful and practical information. Some of the videos had sound issues and minor editing glitches. I wish there had been more hands-on assignments. The few that were supplied were good but I think one more, less guided example would be good.

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.