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

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46,496 classificações
5,334 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.

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.

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4776 — 4800 de 5,278 Avaliações para o Structuring Machine Learning Projects

por Pablo L

30 de Out de 2017

Great set of guidelines. Mostly theoretical, though.

por Cristina G F

22 de Out de 2017

Concrete reminders of important and practical points

por Ktawut T

10 de Out de 2017

Very useful materials for leading a ML research team

por Awalin S

29 de Set de 2017

interesting insights about real world implementation

por Yu L

3 de Abr de 2020

would like to have more excercise related to coding

por Mage K

7 de Mar de 2018

Would've liked to have some programming assignments

por Carlisle

20 de Ago de 2017

Introduced a lot on engineering project experiences

por Marcelo A H

29 de Mai de 2020

Very interesting topics were shown in this course.

por William L

17 de Abr de 2020

Very useful knowledge that is not commonly taught.

por Alvaro G d P

27 de Nov de 2017

Interesting but perhaps we could have gone deeper.

por John H

26 de Ago de 2017

Is the flight simulator hw going to be added soon?

por Pat B

8 de Dez de 2019

Great course. I liked the compact, 2-week format.

por liu c

17 de Mar de 2018

A little bit abstract. But still very inspiring!

por Florian M

24 de Ago de 2017

Very interesting tools and ideas for applied ML.

por Nicholas N S

28 de Abr de 2021

There is so much noise in the explanation voice

por Jason G

24 de Nov de 2018

Not as strong as the other 4 of 5 of the series

por Mark

12 de Out de 2018

Great course. Needs deeper practical examples.

por Francis J

25 de Fev de 2018

A lot of insights rather than technical details

por Lukáš L

7 de Jan de 2018

Coding exercises would be great in this course.

por Mares B

17 de Nov de 2020

A little short, maybe more hands on exercises?

por Ed G

8 de Nov de 2020

Concise course with some interesting concepts.

por Tulip T

23 de Jul de 2019

Quite helpful when you start a new ML project.

por S V R

4 de Nov de 2018

The session were simple, could be more complex

por Caique D S C

30 de Jul de 2018

very good course, could be less massive though

por Виницкий И В

11 de Dez de 2019

I want a program exercise like in 1-2 courses