Muy buen curso! Me hubiese gustado que se desarrolle un poco más TensorFlow. No me quedo claro por ejemplo, cuándo hay que inicializar variables, si es realmente necesario definir las constantes con tf.constant, etc.

The explanations are amazing. I do not qualify with 5 stars as I think that practice can be structured in a better way. If the practice is done after each module in each 'week' it would help to retain main concepts.

Good course overall. but labs could be expanded. Don't know if the Coursera platform supports it, but labs between lectures about different topics would be nice instead of having all practical exercises at the end.

Nice course, as always!

But I think the hyperparameter tuning methods are hopelessly outdated / missing the most promising current developments. A pity since this is such a central part of the actual work with DNNs!

You can get a better insight as to how to improve neural networks that go beyond the fundamentals. The quizzes and assignments helps you get a hands-on experience of the theoretical material covered in the course.

Hi

I enjoy the course a lot!

for tensor flow - I am not sure if its me or the course - but I need much more training to start thinking the tensor flow way. maybe i will practice more on real work cases.

thanks !

Oriel

You've learned deep neural nets but on the first problem you apply them to they seem to not work or learn to slowly. Don't panic, all you may need is a little fine-tuning, that is what this course will teach you.

After this second course you will be able to start build things using Tensorflow. Really great to see how good this course is structured. Things from course one is comming back making it easy to grasp new content.

Overall, the course is interesting and introduces systematically technical details. There are still some confusing part in the assignment. For example, the direction in the last assignment is kind of misleading.

The explanation and examples are very informative throughout the course. The quizzes and the assignments are highly related to the topics covered in the videos which provide a solid understanding of the course.

Some very interesting consideration, though I would have liked a section about reproducibility and randomisation (including for GPU trainining), though I understand that this is framework and language dependent

Some of the programming assignments are a bit confusing, and the grader seems to suffer from bugs at the moment. Nevertheless I found especially the part on optimization algorithms very helpful and interesting.

Maybe TF should be thought a little earlier with small exercises in the weeks 1 & 2. Also the final programming assignment should be improved. The seed initialisation at the Xavier initializer is ambiguous.

The insights and intuitions Andrew communicates are good, but as he starts to point out towards the end of this course, in practice one uses a DL Framework and you don't code these things from the ground up.

The level of detail in this course really leads to a good understanding. A bit more programming exercises with TensorFlow (more than a single model) would be good to understand the intricacies a bit better.

Extremely well designed course, the key reason for 4 stars is Andrew Ng's amazing leactures. The programming assignment though do quite a bit of handholding which can be reduced.

Amazing experience overall!

The course is great for beginners, and I'll recommend watch the vid with Deep Learning on MIT Press. The only cons for me is that subtitle is toooo big, I wish I can change the font size on the vid setting.

Very short, but very interesting. Some more advanced topics are presented that students don't typically learn on coursera courses, such as improvements to gradient descent, batch normalization, and dropout.

Good course, not quite as intuitive as the first course in the specialisation 'Neural Networks and Deep Learning' but still very good. Its also great to have some exposure to Tensorflow through the course,

Great course. One star was taken off, as I would like to see more in-depth info on Batch Norm and a bit more discussion on how to compute gradients in case that is used. But generally that's a minor detail

The course is a good continuation of the first one. Only criticism is that it uses an out of date version of tensorflow as the final assignment. It has a completely different syntax to modern tensorflow.

This was a great course....but at some places I felt that the details have been hided a little....only in few videos.........but overall it was a great course.....best of the courses...I have ever seen ..

This course helped in deepening knowledge about optimization techniques and how they could make ML/DL algorithms robust while training. This also provides a good introduction to the Tensor flow framework.

The lectures are all really good, but the programming assignments feel like they hold your hand too much. It's very easy to sort of slide through them without having a good understanding of the material.

Everything till now was good, But I can't tell why my forward propagation method is rejected although it matches the expected output. So my marks were deducted for it without any reasonable explanation.