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

4.8
estrelas
44,630 classificações
5,053 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

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.

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!).

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4451 — 4475 de 4,999 Avaliações para o Structuring Machine Learning Projects

por Shirley Z

6 de Nov de 2017

Could be more examples or coding assignments for transfer learning.

por Chao L

29 de Set de 2017

Distills the key and practical aspects of machine learning projects

por Chuyi H

2 de Jul de 2020

the knowledge is very relevant and the quiz is fun and challenging

por Tomas O H

19 de Jun de 2019

Very useful intuitions, needs a little more of coding if possible.

por Aniceto P M

24 de Abr de 2019

This course is a bit short but there i a lot of experience bottled

por Javier F P V

19 de Out de 2017

Programming assignments for transfer learning would have been nice

por Manish C

29 de Jan de 2020

Nice to learn skills about project handling in machine learning.

por Luis E G

23 de Dez de 2019

A little bit boring and repeated info., but still valuable stuff.

por Lili W

19 de Ago de 2018

Tooo much time to repeat boring things...but still a good lesson!

por Nicholas K

13 de Mai de 2018

Worthwhile: good info and the practical aspects of tuning models.

por Alexander B

24 de Abr de 2018

Good for understand how to spend your time on DL projects I guess

por Kunkyu L

14 de Set de 2017

It's difficult for me, so I have to retake 3 course, when I need.

por harm l

10 de Set de 2017

Theoretical insights in strategic development of your ML project.

por Ziyi

18 de Nov de 2018

The answer of Questions from quiz 2 seems to be not so confident

por Mohit K

29 de Jun de 2018

Superbly discussed practical problems in the field of ML and DL.

por Ch N

11 de Set de 2017

A programming assignment could be included to learn more better.

por Raimondo M

7 de Abr de 2020

I would have liked some Python assignments on Transfer Learning

por Hamzah A

7 de Set de 2019

It would be better to have some hands on assignment or quizzes.

por Mehmet N

3 de Jan de 2019

Some questions/answers of the quizzes were not accurate enough.

por Erik B

4 de Mai de 2018

Provides a more scientific approach into hyperparameter tuning.

por Qihang S

25 de Mar de 2018

I hope that in Course 3 there are some programming assignments.

por Ashwani K

27 de Abr de 2020

Nicely explained the concepts and importance of error analysis

por Su L

15 de Mar de 2020

Volumns of different vedios are different. Some are too small.

por Yue Z

23 de Fev de 2020

complementary material for the book "machine learning yarning"

por Pankaj R

12 de Mar de 2018

Lot of theory was their. Good but felt asleep after some time.