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Вернуться к Convolutional Neural Networks in TensorFlow

Отзывы учащихся о курсе Convolutional Neural Networks in TensorFlow от партнера deeplearning.ai

4.7
звезд
Оценки: 4,846
Рецензии: 727

О курсе

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....

Лучшие рецензии

JM

Sep 12, 2019

great introductory stuff, great way to keep in touch with tensorflow's new tools, and the instructor is absolutely phenomenal. love the enthusiasm and the interactions with andrew are a joy to watch.

RB

Mar 15, 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..

Фильтр по:

626–650 из 727 отзывов о курсе Convolutional Neural Networks in TensorFlow

автор: Subhendu R M

Aug 12, 2020

A nice well-balanced course.

автор: Walter G

Nov 29, 2019

A very brief quick course.

автор: Guilherme R M

Jun 10, 2019

Bom curso, muito prático.

автор: Loutzidis A

Mar 16, 2020

The quiz were quite easy

автор: Prabesh G

May 23, 2019

Okey.. So easy but okey

автор: Tanguy C

Apr 24, 2020

Thanks. Enjoyed it.

автор: j_lokesh

Jun 15, 2020

that's was awesome

автор: Patrick L

Dec 26, 2019

I like this course

автор: Vivek S

Jun 24, 2019

Super cool stuff!

автор: Paulo A C

Apr 23, 2020

Great content!!

автор: ashraf s t m

Jul 31, 2019

Voice is low

автор: Venkatesh S

Dec 02, 2019

Excellent!

автор: Bingcheng L

Nov 12, 2019

quite easy

автор: Suraj

Feb 11, 2020

Too easy.

автор: Hamzeh A

Aug 06, 2019

Very Cool

автор: Omar M

Jul 16, 2019

Was okay

автор: Henrique C G

Jan 02, 2020

I'm sad to say that I'm really disappointed with the course. What is even stranger is that professor Andrew is associated and endorse the course. I like professor Marooney, but honestly, even his free tutorials on the Tensorflow channel on Youtube have more information than this course. It really seems like something put together in a haste just to make it available on Coursera. The level of detail and instructions is not on par with the quality of both the Coursera platform and the professors associated with this course.

It seems that as I progress through the courses in this specialization the instructions get poorer and poorer and the level of information gets more and more scarce. It got to a point where we are just given notebooks to run; they are not even graded (they barely were on the first course). And even the notebooks where the we are given a chance to complete some code, there are absurd things like "print(#your code here#)" in places that don't even make sense except if we copy and paste from the other notebooks of the course. Really? Print what? The only way we can guess what kind of debug info the notebook is asking is by looking at other notebooks at that exact same line.

For the reviewers; if you are really reading this, please remember that Coursera is charging $49/month for this specialization. If we consider that an average student will take 4 weeks to complete, that's almost $200 for something that's barely a tutorial at it's current version. $49 may be a reasonable rate for a citizen of the US, for example, but it's and exorbitant amount of money for students of poorer countries using the platform in hopes of acquire knowledge of decent quality.

автор: Zoltan S

Aug 02, 2020

After taking Andrew Ng,s truly excellent 5 course specialization, I was hoping that this followup specialization would be at the same high level. In my view (and I am sad to say this) the present course doesn't live up to that expectation.

Of course you could still learn something useful, mostly a selected part of the Keras API. The instuctor is friendly and explains some of the basics of convolutional neural networks. If you are willing to experiment on your own (run the code longer on Colab, play with the hyperparameters, etc) then you get more practice and certainly more out of this experience. Keras has a lot of good tools. For more advanced students going directly to the TensorFlow tutorial website is also an option (and it is free).

Overall the course seems a bit rushed, while it has the potential to be better. Let me suggest adding more basic materials to solidify knowledge (for example practicing hands-on image preprocessing before teaching the Keras preprocessing API and overall more experimentation with images). Also adding more exercises on more diverse topics (GAN's, face detection, variational autoencoders, object detection).

There are also some minor issues (easy to fix): for example right now in the Week 3 HW the prepared callback teaches the students exactly the wrong approach. It stops the learning cycle when the training accuracy improves over a certain threshold, instead of checking the validation accuracy. That is an unfortunate mistake to make in a week that discusses different ways to avoid overfitting.

автор: Michael

Jul 26, 2019

A bit too basic and shallow in terms of conducting the lecture. You are left doing most of the things on your own as the trainer assumes you know. Like using the jupyter notebook, configuring the tensorfow. Some of the google collab books do not work or took too long to load, the videos are too short no notes provided at all. After finishing the course there is nothing to refer to and its starting all over again. Given the level of machine learning course with Professor Adrew Ng, the standard is very high and you will expect that same level. Nevertheless, the concepts are very useful and the lecture explain very well. There level of material left for students to practice on their own,like assignments, notes. Not to be referred to existing material.

автор: Muthukumarasamy S

Aug 04, 2019

Overall learning from this course is less compared to the expectations from a 4 week course. I was expecting to learn variety of TensorFlow implementations for CNN like Face recognition, Object detection. But this course only talks about Image classification. It would have been better if you could also discuss more about implementing various architectures in TensorFlow like ResNets, Inception. Also, You talked only about using sequential layers in Keras and concatenation of layers in Keras is not discussed here. I know all these concepts are discussed in Deep Learning specialization. I was only expecting to learn their implementation in TensorFlow from this course.

автор: Artem D

Jan 29, 2020

I liked the lectures (videos). And I did not like that the course has no mandatory programming assignments. I pay for the course to make myself study. And I believe that there is no study without practice. Hence, this course did not make me study, thus I don't understand why I need this course :-(. And I could find free lectures about TF/Keras (maybe not so good, but free) and/or read the documentation. BTW, I really like Andrew NG's courses, but this one really disappointed me.

автор: Shehryar M K K

May 03, 2020

This course focuses on the teaching of TensorFlow modules related to CNNs and does a good job in introducing some modules of tf and keras for data loading and manipulation. However, it is very light on theory and is only helpful if Deep learning specialization is taken beforehand or in conjunction. Furthermore, this course will need some refresh soon for its modules as it is still using version v1.x of tf as well as some code re-organization.

автор: Zhuang L

Apr 20, 2020

The videos were quite solid. The programming assignments were poorly designed to accept identical answers, but not other solutions that work. This did not evaluate students' creativity and depth of understanding. The Jupyter notebook environment was quite fragile. The resources allocated for each notebook was quite limited. I expect more computer or human resources allocated for each student paying the tuition.

автор: Thomas B

Apr 10, 2020

This course teaches you how to apply CNN to image data, how to augment image data with ImageDataGenerator, and how to do transfer learning. It is very easy to follow, and quite possible to finish in half a days worth of effort. It would be nice to be more explicit with what is required by the grader, as assignment instructions not always are clear.

автор: Bakhtawar U R

Dec 09, 2019

Good but too basic.

Specialization's first course already covered the basic of tensorlfow. This course is suppose to expose to sota topics in computer vision using cnns. The content in this course can be easily fetched from many online forums. Thus the curators need to put some advance topic like attention, spatial transformer etc etc