Really amazing experience with this course perfect start after knowing the mathematical concepts of how nn works

Very good for those people who have knowledge of deep learning and want a hands-on in TensorFlow 2

Great and clearly taught course for anyone with basic knowledge of Python and neural networks.

Well defined course. I got a very good experience. well planned handson session. Thank You

Provides detailed coverage of tensorflow 2's fundamentals. Thoroughly enjoyed the course.

An excellent course for those who want to learn how to implement tensorflow2.0

Very good course but the final peer graded review takes alot of time

The best course if you want to get started with Tensorflow 2.0

It provides a systematic way for me to learn TensorFlow 2.

It is an excellent course for beginners in Tensorflow

Excellent course for getting started with Tensorflow.

Loved the course. Really practical and hands-on.

Thanks for all, really useful course!

Very clear and well organized.

Awesome course. Learnt a lot

Great overview of TF2

Great course.

Good beginner

First of all, this course is not a deep learning course but a TensorFlow2 course, so prior knowledge of deep learning is required. For example, if you don't understand what CNN (Convolutional Neural Network) is, it's recommended to take a dedicated deep learning course, such as OpenAI's, before starting this course. With that in mind, the content of this course is excellent. You will become proficient in executing what you imagine using TensorFlow2. However, there are some drawbacks. Almost every module consists of the following components: 1. Videos where the instructor explains concepts while showing code. 2. Videos where the TA (Teaching Assistant) explains concepts while coding. 3. JupyterNotebook exercises. 1 and 2 are quite redundant. Moreover, video 2 involves typing out code from scratch instead of explaining pre-written code, which makes the videos unnecessarily long. Typing out code while watching the video was quite painful. In addition, the lab's automatic grading tool was a bit unstable. Particularly, during the second week's auto-grading, it would fail when the lab was working correctly, and pass when errors occurred while coding as per the instructions on the lab. Additionally, there were issues with the peer review assignment, as attempting to generate a PDF as instructed resulted in a server-side error, preventing the output. I downloaded the Jupyter project and struggled to generate a PDF from my local JupyterLab.

FYI This uses tensorflow 2.0 would be nice to have an updated version for 2.4>

The perfect course to follow up the Deep Learning Specialization. I left that specialization feeling like I had an acceptable academic understanding of deep learning, but that I would be unable to apply it in practice. This course was great for solidifying that pragmatic component.

I read some of the most helpful comments before starting and one of the biggest frustrations was the auto-grader failing. That was not my experience at all. As of this post (5/2021) the grading system worked completely fine for me via Coursera's notebook system.

A concern among other TF specializations was that they did not provide any information that could not be gained outside the doc examples. For this first course, at least, I found the material well exceeded the explanations in the TensorFlow/Keras docs in terms of quality. The labs were very thoughtfully structured and provided the right amount of application to cement the concepts mentally.

tldr found this course to be very worth the time and money

This is just a wonderful course!

Previously I've taken well-kown Machine Learning course and Deep Learning specialization by Andrew Ng. After completing these courses, I had the feeling that I had received a good knowledge of the theoretical aspects of Machine and Deep Learning, but I had no understanding of how to make some even simple models from start to finish (I am not criticizing these courses, most likely the problem is in me).

And this course was a real godsend for me. This course is completely dedicated to practice, this is exactly what I needed. The information presented in it is absolutely clear and contains everything you need to complete programming tasks and to start the independent development of simple models in tensorflow.

No doubt, 5 stars!

Beginning is too basic for me but I like this course because it's a well structured tutorial-like course if you want to learn Tensorflow. First course is about Sequential API, second about Functional and Subclass API and the last about Tensorflow probability library.

It's not going to teach you basic concepts of deep learning like deeplearning.ai specialization but this specialization (its first two courses) is significantly better in explaining how to actually code with Tensorflow - it introduces very early the callbacks and optimization techniques directly into code labs in a way you can see and understand what happens.

I really liked this course. The pace is very good. The focus is also great.

The final project (Capstone Project) is really good practice, I'm not super experienced in ML so doing a kind of "real world hands on project" fell great, even if I spent a lot of time doing it (I made an initial wrong assumption about the data structure which made the model trainings take forever).

You are going to need to know about Python and Numpy, maybe a little Pandas too.

My only constructive criticism would be that it would be great if you got a good mic… these makes videos much more pleasurable. Audio isn't great.

While not super hard, it is definitely a lot more challenging than the introductory course on tensorflow from deeplearning.ai. The structure of the course is excellent, and the capstone programming assignment is actually decently hard.

I also liked that the programming exercises aren't fill-in-the-blanks as they are on deeplearning.ai, and require some thought. This course deserves way more students than have currently taken it. Easily one of my top 5 courses on coursera.