This course was excellent, however the Tensor flow at the end feels a little bit like the ML field is quickly being overtaken by the frameworks, and the Tensor flow section is a little bit tacked onto this course, maybe in a hurry.

Interesting practical suggestions regarding hyperparameter tuning and batch normalization, it could be more mathematical with more programming assignments with the effects of tuning. The TF framework was kind of surprise in Week 3

As always, great course from Andrew: easy to be understood, useful trainings and exercices. The lecture are explained slowly and repeating the important concept, always a good think.

Thanks! I will proceed with my Specialization :)

The content of the course is excelent and the practice exercises very interesting and helpful. I feel there is missing a written resume of the concepts after each video, or a hand-in of the powerpoint presentations would be nice.

Pros: The course covers the basics for hyperparameters, tunning, regularization and optimization. The basics that it covers are well presented and explained.

Cons: A much more detailed and intense work with TensorFlow is needed.

The course is really good and the explanation by Dr Ng is exhaustive. But I think the assignments were meant for beginner level. You will not be implementing any function instead you be writing the underlying formulas. That' it.

This is another great introduction to Depp learning frameworks apart from all the neural network performance upgrading techniques taught. This is an excellent course for building a strong foundation of deep learning fundamentals

This course was mostly well-designed especially for the first topics, but in my view, the Tensorflow part needs to be extended. It provided a brief understanding of the topics, but I do believe deeper examples might be helpful.

better than the first course since it involved breaking into new stuff w.r.t the Stanford Machine Learning course.. However, altogether not yet challenging enough to give 5 stars

Really interested to go deeper into this matter..

Interesting material but a bit complex to follow all the equation derivation. Need to repeatedly watching the video to understand the content. After learning this the hyper parameter setting in the ML setup is clearer to me.

I would like to have harder programming assignment, perhaps optional. Because instead of understanding the problem I just had to read the question again, answer were always in questions, then copy paste and change "x" by "X"

Overall, it's pretty good. I did have a problem understanding some of the facts being communicated about gamma and beta in batch norm. Also, I think there is a problem with the last notebook. My cost did not go down as fast.

I think the last subject, the "gradient checking" looks a bit not connected to the begining of the week 2 course, it may be only me, so I will go over it again and try to understand its relevancy to the rest of week 2.

Teddy

Good choice of topics to cover. I think the central idea of some of the videos could have been summarized much more quickly, in a minute or two, with the rest of the time spent going into more depth if the student needs it.

The content of the Course is very precise and assignment truly reflect what is been taught in the lectures. Explanation and presentation of algorithms are what I like the most. Assignment were very engaging and interesting.

The class should include more introduction on the current ml frameworks such as tensor flow etc. Possibly it should include one more project for the ml framework. Hope to give students more experience on the ml frameworks.

Good and detailed information on how to tune parameters, optimization techniques and regularization. I'm confident that this course learning will help me in training NN to better convergence in a shorter time than earlier.

I would have liked more details on the math. Furthermore, I think that the discussion of TensorFlow was a bit too short. Although I was able to do the assignment I have not yet developed an understanding of TensorFlow.

Excellent. Mon regret est que l'exercice final ne mette pas en oeuvre le tuning des hyperparamètres sur un jeu de cross validation. Un exercice supplémentaire avec TensorFlow ou Keras sur cette notion aurait été un plus.

Very Interesting

As usual, it is very good from theory point of view. Practical examples are also really interesting.

Do not expect to be autonomous after the course, as you won't be able to use Tensorflow or Keras alone.

I'd make the tesnsorflow section a separate week with much more elaboration, the first time (in both course 1 and course 2) I felt a subject was lacking information. It's mostly noticeable in the programming assignment.

The course really takes the student by the hand through the exercises. The disadvantage is that it is not really necessary to understand what you are doing. Just follow the guidance. But on the whole really satisfactory

Very exciting and interesting course overall but the programming assignment with Tensorflow was not practical in my opinion. Instead having practical experience building NN with Keras might have been the better choice.

Generally the course is very good! I liked that I could manually implement the steps of hyperparameters tuning. I wish there was a bit less boilerplate code. Implementing everything from scratch would be more valuable!

Could be Greatly improved by having us build a NN using previous learning's with the only change being use of SoftMax for Cost. Then have us use TF to do the same and compare the code effort, and the results 1-to-1...