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Comentários e feedback de alunos de Convolutional Neural Networks in TensorFlow da instituição deeplearning.ai

4.7
estrelas
6,742 classificações
1,049 avaliações

Sobre o curso

If you are a software developer who wants to build scalable AI-powered algorithms, you need to understand how to use the tools to build them. This course is part of the upcoming Machine Learning in Tensorflow Specialization and will teach you best practices for using TensorFlow, a popular open-source framework for machine learning. In Course 2 of the deeplearning.ai TensorFlow Specialization, you will learn advanced techniques to improve the computer vision model you built in Course 1. You will explore how to work with real-world images in different shapes and sizes, visualize the journey of an image through convolutions to understand how a computer “sees” information, plot loss and accuracy, and explore strategies to prevent overfitting, including augmentation and dropout. Finally, Course 2 will introduce you to transfer learning and how learned features can be extracted from models. The Machine Learning course and Deep Learning Specialization from Andrew Ng teach the most important and foundational principles of Machine Learning and Deep Learning. This new deeplearning.ai TensorFlow Specialization teaches you how to use TensorFlow to implement those principles so that you can start building and applying scalable models to real-world problems. To develop a deeper understanding of how neural networks work, we recommend that you take the Deep Learning Specialization....

Melhores avaliações

MS
12 de Nov de 2020

A really good course that builds up the knowledge over the concepts covered in Course 1. All the ideas are applicable in real world scenario and this is what makes the course that much more valuable!

RB
14 de Mar de 2020

Nice experience taking this course. Precise and to the point introduction of topics and a really nice head start into practical aspects of Computer Vision and using the amazing tensorflow framework..

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1001 — 1025 de 1,041 Avaliações para o Convolutional Neural Networks in TensorFlow

por Dhruva G

21 de Ago de 2019

The content could have been covered in 15 min. Moreover, I thought you guys will teach tensorflow low level API and estimators etc. atleast in course 2. Also, what happened to the graded assignments ? I finished this course in 40 min.

por Stephen M

4 de Mai de 2020

The course simply does not cover much information. The whole course could be compacted into a decent one hour lecture. Andrew Ng has some great courses on machine learning but I don't believe this to be one of them.

por Kalana I

5 de Jun de 2021

The course material is good and the lectures are great but rating it low to bring attention to the assignments which were old and incomplete. They really need to be updated. Specially week 4.

por Klemen V

7 de Jan de 2021

In my opinion there is to little background explanation. There were errors in Programming Assignment

in week 3 and 4. I had to look in forum discussion so I could complete the exercise

por Dmitry T

28 de Dez de 2020

weeks from 1-3 were perfect.

But the programming assignment on week 4 needs to be fixed. Please add hints and examples, otherwise it is just a headache

por Jose R

26 de Jul de 2020

No enough time spend in the actual code which limits the learning on the understanding of the concepts with implementation. Doubt how useful this is

por Muhammad R R M

12 de Abr de 2021

Last exercise is so bad. It didn't even discuss about flow function, why it's need 26 last layer? isn't it should be only 3?

por Dmitriy S

12 de Nov de 2020

Absolutely awful grader. You spend most of the time figuring out errors and intentions of the grader writer.

por Matthew R

17 de Dez de 2020

Very superficial look at deep learning. A lot of the programming assignments had little to no context.

por Francisco R G

29 de Set de 2020

Repetitive chapters, repetitive info on videos, and not very useful final test. They have to review it.

por Jin C

27 de Set de 2019

It's too easy for an intermediate machine learning leaner, and it's little about naive TensorFlow.

por Roger G A

11 de Out de 2020

Specially in week 4, big gap between information taught in the lecture, and the last assignment.

por Pouya K

9 de Fev de 2021

poorly designed exercises

poorly designed material that all could be said in just 1 or 2 weeks

por Jair N

5 de Abr de 2021

The content are good, but the audio is low and the exercises are not well documented.

por Adith k

1 de Jun de 2021

very basic.

There's hardly any video time. We can finish the whole course in a day

por Apoorv V

1 de Ago de 2020

Average content. The last assignment for week 4 was structured quite poorly.

por Parth

12 de Set de 2019

coding assignment should be included otherwise it is easy to get certificate

por mukul k

4 de Abr de 2020

expecting more advance topics instead of just using Keras.

por Oliver J

3 de Nov de 2020

The last assignment is poorly designed and a real pain.

por Hanzhao L

11 de Fev de 2021

The practices were poorly designed.

por Cynthia E

30 de Mar de 2021

Assignments are not well designed

por Gunes D S

12 de Jan de 2021

Very little content, ridiculously repetitive. Missing info in the last assignment made me waste a lot of time. I wouldn't mind spending time on that assignment if it had been actually challenging. Course designers should consider putting serious time and effort into the preparation of the hands on materials. There is some useful information in this course, but I passed without learning much because of poor design. I still have no idea how to design a good model based on the data set, how many filters to use in each convolution, how many layers in total, how many nodes in the dense layer, etc. The instructor keeps recommending "trial and error", which is fair but I would expect the discussions to be a bit more thoughtful and deeper than that, especially given the price of this "professional certification". This course is falsely advertised as intermediate level, it is actually introductory level, albeit being too simplistic compared to some other great substantial introductory level courses on Coursera.

por Alessandro S

3 de Nov de 2020

Honestly this course was a bit of a disappointment, didn't really learn anything that can improve how effective i can produce a neural network, on the assignments the hardest parts was coding tasks that was never explained in the course, like reading and copying files, and that has not really anything to do on how to build a neural network, some of the examples provided did not really worked (as the accuracy was poor and always over-fitting), for the last assignment i cheated as using image generator lead to a really poor results so i just skipped and train the model using directly the images.

My suggestion spend more time on what strategy are used to improve the model when the results are poor as the model under-fit or over-fit, otherwise it looks like you add and remove layers until you got lucky.

por Fabian A R G

2 de Jun de 2021

I am sorry for the 1 star but is time that deeplearningAI take course content difficulty a bit more precise. I know and I appreciate a lot what you are doing on spreading the knowledge of AI through many varied content, nonetheless if you put that the course is intermediate you should aim to make it intermediate. This is pre-introductory level course and I would have appreciated knowing that so I just skip it an go through a more advanced option. I still finished it as I already paid for it. It is still a well organized and fun course as usual, the problem is that this has absolutely no value in terms of "real world" examples as you put it...

por Tal F

13 de Ago de 2020

What a disaster! I spent more time trying to game the scoring service into accepting a correct answer (and reading in the forum how other frustrated users managed to do it) than actually learning about tensor flow. Such a shame because there were some interesting topics. There is no TA answering questions in the forum either. It doesn't feel like any effort has been made to keep the course current and updated, addressing issues, etc. And the lab work was actually very repetitive - some were almost identical to previous ones.