Learner Reviews & Feedback for Convolutional Neural Networks in TensorFlow by deeplearning.ai

Super cool stuff!

A good course for Keras but not for tensoflow.

Was okay

I think the quiz should be harder

Voice is low

I think I might say this for every course of this specialisation:

Great content all around!

It has some great colab examples explaining how to put these models into action on TensorFlow, which I'm know I'm going to revisit time and again.

There's only one thing that I think it might not be quite so good: the evaluation of the course. There isn't one, apart from the quizes. A bit more evaluation steps, as per in Andrew's Deep Learning Specialisation, would require more commitment from students.

Transfer learning part should have been in more detail. Thanks.

Very Cool

The final assignment was somewhat a steep step

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.

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.

Okey.. So easy but okey

Exellent tutorial for using Tensorflow and convolutional networks. Useful usage examples, interesting and challenging exercises. A few minor mistakes prevent five star grading. But please note that mistakes happen and we have to live with this :-). Nice work, looking forward for the next course of the specialization.

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

A well taught course with interesting coursework and projects

I need more coding practice, not just quizzes.

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.

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.

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

Please transfer the notebook from CoLab to Coursera.

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

Some overlap with first course.

not challenging enough

Could have dived more into the details and inner workings of Convolutional layers but overall awesome course.

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.