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

4.8
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46,489 classificações
5,332 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

AM
22 de Nov de 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.

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.

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5051 — 5075 de 5,276 Avaliações para o Structuring Machine Learning Projects

por Eric M

20 de Out de 2017

A fundamentally very good course with a few technical gltiches that can be easily corrected and some confusing elements to be clarified.

por Bongsang K

21 de Mai de 2018

I think this lecture is important for every research scientist. However, there was no programming examples so I was confused sometimes.

por Michael L

1 de Mai de 2018

No programming assignments or labs, so too much theory, and too little chance to put same into practice. Not a good value for my money.

por Max S

13 de Dez de 2017

Still good but getting much sloppier. Bad editing of the videos, some exercises plain wrong and staff not reacting to forum posts, etc.

por Xiang L

26 de Abr de 2021

This session might not be very helpful for people from different backgrounds such as non-industral level application of deep learning.

por Lars L

30 de Dez de 2017

Course materials need some cleanup. Were a number of audio blips, in the video. Material was good but just didn't seem as polished.

por Nitin S

25 de Jun de 2020

Decent learning. Though quite some stuff, I felt as repetitive and obvious.

I wish there was some programming exposure as well here

por Taavi K

29 de Nov de 2017

Too short on its own (took half a day to go through the whole thing), could have been combined with Course 2 of the specialization.

por Raghu t D

6 de Ago de 2018

this session was good it would be more better if they provided the code of them..so that we could be abke to learn more from them

por Denys G

23 de Nov de 2017

Felt a bit rushed, each video was full of good tips but personally I think each video should have been a jupyternotebook instead.

por Massimo A

18 de Nov de 2017

More theoretical than the other courses in the specialisation but still very high quality.

Short but with a lot of information.

por David P

17 de Out de 2017

Not nearly as good as the first two courses. These two weeks should probably be added into the second course at some point...

por Oliver O

16 de Out de 2017

Would like more applied discussion and for it to be Longer. In particular I would like to see a discussion on class imbalance.

por Shuai W

19 de Set de 2017

The content of this course is a bit too little for me.

However, it provides useful guidance for my projects. Much appreciated!

por Gary S

15 de Set de 2017

Not nearly as valuable as the first Deep Learning course. And the questions posed in the quizzes seemed far more subjective.

por Pejman M

21 de Out de 2017

Programming practices with TensorFlow should have continued in this course. Unfortunately, these two weeks were all talking.

por Nithin V

3 de Jan de 2021

Need more quizzes, assignments to deepen the understanding, But otherwise thank you Andrew Ng for presenting this material

por Panos K

18 de Abr de 2021

The pace of the first part of the course was too slow. The second part (from Transfer learning onwards) was much better.

por Mustafa H

16 de Jul de 2018

This course does discuss interesting and important subjects but I feel it can be combined with course 2 of this series

por Ahmed A

10 de Jul de 2018

course is very good have a lot of important theory, it will be amazing if become 3 weeks with programming assignments.

por Kevin Q

19 de Mar de 2018

lot of issues with assignments and ambiguous quiz questions this time around, not as polished as other Andrew courses

por Arghya R

19 de Set de 2017

Could have more case studies and above all. Also programing assignments on self driving car could have been better

por Okhtay A

5 de Abr de 2020

A bit too free form compared to the other courses in deep learning specialization, but maybe that was the goal.

por Masih B

18 de Jul de 2020

This course could be way more better, if it also focused on codeing with tensorflow (like the previous course)

por Janet C

29 de Jun de 2019

Overview of the machine learning process. No projects or sample code to actually organize the ideas into code.