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The course was good introduction to tensor flow I learned lot of basics which otherwise I could not have learned from books or other online materials. The concepts are well explained. What I am not happy is about the Datascience labs. In places where internet is slow it is very difficult to do it. Instead of this in we are provided some alternate instructions to run them on a local machine that would have helped at least for some of the first few labs. I know that all of them cannot be run on local machine then the whole purpose of learning tensorflow on Google Cloud is defeated. The whole purpose is to learn how to run it on a cloud environment with scaling. I know that is not possible on a local machine. Another option would be to provide instructions to run the code with without notebook. I basically do not like notebooks , I Prefer command line to notebooks to execute and see results live. But overall I got a good intro about tensorflow - Thankyou very much.

First of all we need to understand that TensorFlow is not just a Python toolkit. It's a complete tools from Python library, training management, monitoring, down to deployment to cloud or what have you. Therefore this course should be viewed as getting started introduction to ALL of that, not just the toolkit. And I think it's quite good. There are few glitches here and there when it comes to interacting with the GCP, but that's fine, you're learning something while fixing it. The disappointment comes from the forum though, as the staff's only response seem to be to shift the responsibility to Qwiklabs

An excellent introduction to TensorFlow, Including debugging tips, and how to scale up TensorFlow models and deploy them. So why only 4 stars ? because there is no audit option for this course and the videos can't be downloaded. Presumable the notebooks with sample code can be cloned from Github - but it seems the explanations will not be available unless you re-enroll. This policy is even more inexplicable considering that the course serves as a "presale" for the Google cloud platform.

At a couple of important points in the course (e.g. where it is about launching TensorBoard or even more important where it is about deploying the model with ML Engine) the code in the Lab differs substantially from what is shown in the discussion of the lab. This is a little irritating. That aside, I have learned a bunch of new techniques and processes to improve my coding and especially: code more quickly and scalable. Thanks for some really good lessons.

Very useful but I had some problems with lab infrastructure. Options to create buckets wouldn't appear sometimes and I had to open and close google cloud console to make it work sometimes. Regarding the course it was great but there is a lot of boilerplate code and though the steps are simple and clear there is a lot to digest, I will need much more time master this TF/GCP workflow, but anyway this is a great start.

I think a lot of the lab-explanation given in the video following the qwiklab should be in the python notebook; make it a little more illustrative (e.g. architecture diagrams). Also, be a little more generous with the lab time - the last lab was too long (or perhaps change the code to select the faster ML option - standard/TPUs etc. to make the training go faster)

One of the lecturers should improve his English speaking. The course should add more contents, explanations, and exercises for the 3rd part of the course regarding how to scale TF models with CMLE, for example, some bash cmds or some code are confusing, unless this content will be covered more in the following courses.

I could not get my final lab project to work. I have sent the issue to Qwiklabs - I got the following error message:

ls: cannot access '/home/jupyter/training-data-analyst/courses/machine_learning/deepdive/03_tensorflow/labs/taxi_trained/export/exporter/': No such file or directory

I enjoyed this course a lot. If I could modify anything, I would adjust the content and pace of the third week. The videos are relatively simple to understand and well-explained while the final lab feels a lot harder with a lot of unknown command to execute.

hands on demonstrations were good. More in depth explanation can be done fro some of the codes including the part in which data fatching from the json file was explained, and the process to be followed in the gcp to make the model and deploy it.

it was really excellent course to take, some of the complexities in the videos could have been easily explainable and vocabulary could have been easy for every age group for understanding,

otherwise it was amazing experience learning

Excellent course in the capabilities of tensorflow, the course material and data-lab examples are super useful and provide a good overview of how to implement tensorflow models locally and in the cloud with high-quality practices.

Challenge problems at the end of each assignment are really good, however, there should be videos showing how the instructors would solve them, I would be fine watching 30 min videos describing the solutions. Nice course!

Intro to TF should have packed with more fundamental concepts around TF alongside existing topics covered. Moreover, some of the code needs either further explanation or references to understand what a given code is for.