Very insightful course on improving deep learning models. Just in the course details the level of course should be intermediate rather than beginner level.

It was one of the best lectures in Hyperparameter Tuning for Deep Learning Models. I would really like to thank Coursera for this awesome course, exercises.

Very well organized. The right balance between theory and practice with good hands-on examples you can exercise without boring details of language syntax...

I like the idea of introducing framework like Tensorflow. But Week 1 and Week 3 lectures are too long, maybe separating them into different weeks is better?

More details about deep learning. This gave a more thourrouhly understading of the deep neural network and how to deal with the details i met when using it.

Very important course if you have already taken the deep neural network course. The course make sense of all you learned previously on deep neural networks.

this course contents and the way it was laid out and expressed was so much cleaner and detailed than udacity or any other course I have gone through. Kudos!

Clearly explained quite a lot of techniques for optimization of cost function in deep neural nets, including hyperparameter tuning. Very useful to practice.

Buen curso, me gusto mucho ya que aprendi muchas cosas que no conocía sobre regularización y seseo de parámetros. Ademas muy buena introducción a tensorflow

Understanding the mechanics behind the many hyperparameters of neural networks gives a lot more confidence to make decisions when improving your own models

As always, Andrew is absolutely amazing at transmitting advanced knowledge and making it accessible to most people. Thank you Andrew and the whole team !

as empirical technology, deep-learning requires a number of empirical knowledge for running, and this course provides a ship of arms for new practitioners.

Really in-depth knowledge on how to choose which hyperparameters are relevant to your model and how to tune them for optimal performance. Money well spent!

Great continuation of the previous course in the series. Provides plenty of mechanisms to optimize your mode. TensorFlow introduction is also very helpful.

It helps me understand Regularization, Dropout, AdamOptimization algorithm, Batch Normalization, Softmax classifier which have bothered me for a long time.

This course is a really nice hands-on experience on how to tune and tweak the basic methods for increasing performance. I'm really keen on learning more...

Great course, which is part of an amazing Deep Learning specialization. Prof. Andrew Ng is an awesome teacher, who makes this material easy and accessible.

A very interesting course for both material and assignments. It teaches you the details beyond deep learning and the various way to hyperparameter tuning.

Again loved the course structure, Andrew NG - you are a rock star. Take a bow, thanks a lot.

I would love to have one more extra week on tensorflow/keras.

A very good course focussing on very very important aspects to make Neural Networks perform well.All concepts are been discussed in an easy to grasp way.

Learned a lot of interesting stuff to improve the speed and efficiency of the model. Moreover, the introduction to the TensorFlow framework was intuitive.

My interest in the subject is increasing. Just experienced Tensor-flow for the first time. Excited to finish the remaining courses in this specialization.

Great course with lots of important and practical topics that are often ignored grad courses. Thanks a lot, Prof. Andrew Ng and the deepleawrning.ai team.

This was a critical course for understanding deep neural networks and their nuances. I enjoyed it and will revisit it for revision. Great Work Prof Andrew

Nice.

There are some magic functions in TensorFlow which I could fully follow during the programming assignment. Will do some self exploration.

Thanks a lot