I like courses that are longer and more in depth such as the first specialization of deeplearning.ai, I just could not continue that one because financial help always got me financed the first course which I had completed and cannot pay for the other ones

Nice introduction. Tests are too easy. Exercices are easy too, but still is fine, rather I prefer them to be more challenging (like: 'try to play with number of epochs and other parameter to achieve 99% accuracy for example ' and do not guide to much).

This course was a little too basic and introductory, personally. But the course structure really makes up for it. It is better suited for someone who is new into this field. Since I had half a year's experience in PyTorch, this course felt too simple.

I loved the fact that it was more hands-on than theoretical. Although I did expect some more explanations on some parts.. but the suggested links were good enough for those parts.. Overall, I loved it. Lawrence is really good at what he's doing.

Should have provided more explanation about the assignments. The explanation videos about the topics are no longer than 4 minutes. You should know some of the DNN concepts, then you can understands the assignments in depth and more clearly.

Not being able to run the notebooks on Coursera was frustrating. Fortunately, running them on colab wasn't difficult - just an unnecessary impediment.

Was nice to see some of the more abstract deep learning terms be put to use fairly easily.

May be provide resources to learn some senior grammar knowledge for python, because basic knowledge for python does guarantee we could understand the code perfectly, but simply remember its form in case of a particular complex line of code.

I had a background of Andrew Ng's Machine Learning Course so i did not really have any difficulties with it. However, a bit more detail on conv nets' theory will make this course much better. Still, I loved this. Thank you deeplearning.ai!

Good course, somewhat easy, but I anticipate a lot more new and interesting and useful stuff in the remaining sessions yet to be offered. It would be good to point out what is now Tensorflow 2.0. I anxiously await the new material. Thanks!

Great course, excellent contents. I would have loved more smooth intro about the generator as I found we suddenly went to talking about this generator without prior introduction. To be honest, I still cant see the value of this generator!

Good introduction to TensorFlow, but that is all there is.

Don't expect to gain any understanding in how neural networks are and work from here. It is only about learning the TF API.

I think the title is a little misleading in that sense.

Very conceptual course, with few exercises to help you learning how to build a basic convoluted neural network to a dataset of images. I enjoyed it, also because it provides many sources to use if you want to dive deeper on your own.

I learnt PyTorch prior to TensorFlow. Still found this course is very helpful. Thanks to the instructors. The only thing is, I wish all the reading links could be listed down on one page, and all the notebook links arranged together.

Laurence is an expert in this field. The material covered in this course is relatively basic, but I think it is a good introductory course for TensorFlow. I was expecting more / elaborate material for the graded assignments, though.

I took this course for the more low-level API in TensorFlow since I already had experience with Keras. But it was still a fun course to watch and an excellent start for people who are just beginning their journey in Deep Learning.

The week four programming exercise needs some improvements. I can not unzip the data, so I had to download this exercise and finished it in local environment. Finally, I hope some videos can have Chinese subtitle in the future.

This one is basically an intro on how to use tensor flow apis to build neural networks. it is very useful to learn how to use these APIs and some Python code. I'm looking forward to learn more about CNNs in the next course.

very practical courses with examples. May address some knowledge on NN architecture design, such as why place an additional flatten layer before the final layer. Some cases, we didn't need such layer and the reason why?

The assignments in the course felt very shallow and did not go beyond the examples described in the lecture videos. Despite this, the lecture videos break down ML & DL using Tensorflow in an understandable manner.

Laurence Moroney is an awesome teacher, can't believe I learned this such in just 1 single course and now I can classify a sad or a happy face and other cool stuff like classifying humans and horse .Great Course

This course gives you a great knowledge of TensorFlow. It is better if they've also included the basics of ML in the course like Linear Regression, Logistic Regression and supervised and unsupervised learnings.

The course gives a good insight in various aspects of computer vision, using tensorflow. It is also a great platform to exercise tensorflow 'online' using Google Colab- an interesting interactive documentation.

Great information delivered in easy to understand lectures with smooth transitions. Having assignments required to pass the course would provide more motivation to spend more time on the hands-on exercises.

I did get excited about Data generator option , just give the directory of images and it will label automatically for us ! cool .Working on complicated data was also cool looking forward to next challenge !

A wonderful course for those interested in TensorFlow. Although I don't fully agree this is completely meant for beginners, you ought to have some basic python skill set. But, overall a wonderful course.