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Voltar para Convolutional Neural Networks in TensorFlow

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

1,592 classificações
219 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 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 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


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.


May 24, 2019

A very comprehensive and easy to learn course on Tensor Flow. I am really impressed by the Instructor ability to teach difficult concept with ease. I will look forward another course of this series.

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176 — 200 de {totalReviews} Avaliações para o Convolutional Neural Networks in TensorFlow

por Super-intelligent S o t C B

Aug 10, 2019

Very good course that teaches you basics of convolutions, augmentation, transfer learning. Thank you to Mr. Moroney and the Coursera team for making it available.

por William G

Aug 16, 2019

It was good, but similar to other learners I feel a little light in content. Though in tandem with the deep learning specialization gives a good view on convolutional neural networks as well as its implementation in tensorflow.

por Xiangzhen Z

Aug 18, 2019

a little bit too easy compared to Andrew Ng's deep learning course.

por Muhammad U

Aug 18, 2019

A well taught course with interesting coursework and projects

por Saeif

Aug 20, 2019

This is another great course in the specialization. I wish only there were graded exercises like the previous course that we can submit and get a grade for. I understand maybe this is due to the long time of training and that is not possible to do.

por Zhi Z

Jul 06, 2019

A good course for Keras but not for tensoflow.

por Nicolas

Aug 30, 2019

First, I think the course was great, very instructive. Thanks to Andrew and Laurence for putting this together, is a great source of information to understand more about DL. Some things I think could improve the course.

I found the transfer learning lessons a bit unclear and I struggle generalizing this to other cases. Also, I was a bit confused by the flow of the course. The course starts with a multi classifier (or actually, the previous course), then the lessons focus on binary classifiers and it ends again with multi classifiers, because these should be the more complex ones.

One last technical thing, only on the last lesson of this course it is mentioned that the classifiers output the probabilities on alphabetical order when using ImageDataGenerators (or at least, that's my impresision). I've wondered since the course introduced the ImageDataGenerators, how the probabilities are assigned on the outputs. I could figure out on the sigmoid that the classifier would look for the first class on the directory and output 1 or 0 based on that, but it would be good to have this mentioned at some point on the video when the ImageDataGen is introduced.

Thanks again! Great course

por 陈浩然

Sep 05, 2019

Please transfer the notebook from CoLab to Coursera.

por Dr. H H W

Sep 06, 2019

Great insight for the practical aspect of TensorFlow, add value on top of Andrew's DL courses.

por Kailyn W

Sep 09, 2019

I need more coding practice, not just quizzes.

por Xinhui H

Sep 16, 2019

Some overlap with first course.

por Anand H

Sep 12, 2019

One challenge i have faced is with deploying the trained models. I find very little coverage on that across courses. It's one thing to save a model.h5 or model.pb. It would be nice if you can add a small piece on deployment of these models using TF Serving or something similar. There is some distance between just getting these files outputted and deploying. TF documentation is confusing about some of these things. Would be nice if you can include a module on that.

por Marcos V G J

Sep 25, 2019

Good content, but lacks exercises that forces us to code ourselves to solve the problemas

por Gerardo S

Sep 27, 2019

the last exercise needed a big upload, made it imposible (for me) to do. This was a problem not related to the subject, should use data downloadable directly from internet.

por Przemysław

Sep 27, 2019

Instructors are really good, but in my opinion, this course should contain Object Detection and Object Segmentation topis.

por luis a

Sep 30, 2019

The course was fine sometimes I feel too easy. I would like to see more of the available options for the layers, such as padding, stride. filter size, mean average, batch normalization, etc...

por H M A r

Oct 02, 2019

The course is really nice. But would be better if the convolutional layers were a bit more detailed. It was a bit difficult for me to understand all the parameters e.g: input/output filter size.


Oct 02, 2019

A more advanced course would be highly appreciated.

por Vittorio R

Oct 06, 2019

Good, but expected more, for example object detection.

por Damon W

Oct 08, 2019

Good practical course. A bit heavy on visual images, but very informative.

por Donal B

Oct 18, 2019

Excellent course. Would have liked graded coding assignments like in the first course.

por Luiz C

Jun 11, 2019

not challenging enough

por Renjith B

Jul 15, 2019

Good content for classification tasks. But didn't cover anything related to object recognition, localisation and semantic segmentation which are the challenging computer vision tasks.

por 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.

por Joey Y

Aug 04, 2019

The course seems to be getting more loose than the first course.