This course helps to learn TensorFlow for Deep Learning application. Deep Learning neural networks which are implemented in Neural Networks and Deep Learning Course is implemented here with TensorFlow.

Wow, this course was very thorough and deep into the underpinnings of deep learning. Thank you Coursera, deeplearning.ai, and thank you Andrew Ng.

You make learning so much fun and accessible worldwide.

I appreciated the explanation of the different techniques (esp. the optimization ones) covered by the course, as well as the effort put in making the notation as simple as possible. Definitely helpful.

This was a fantastic course. Andrew Ng explains these concepts so clearly and provides practical examples that enhance the learning. Thanks again. I just loved the course as well as its predecessor.

This is awesome course. I never seen any tutorial like this on internet. Your language is simple enough and concept are crystal clear. once again Thanks for giving me opportunity through Financial Aid.

Continues a great specialization track. The coding is made to be not at all difficult but still manages to impart the intuitions, such that the lectures and coding assignments do complement each other.

This course is challenging but Andrew explained it so well and the exercises were structured so carefully that you can definitely learn it well as long as you followed each video and did each homework.

Amazing course which focus on the theoretical part of parameters tuning, but it needs more explanation of Tensorflow, as I felt a little lost in the last project. Except that, it is an amazing course.

As a beginner who learn machine learning for 2 months, this course guide me to the basic concepts of hyperparameter tuning! I think I can come back to here while I practice machine learning projects!

Everything, Everyparameter in neural networks looks familiar to me now. I feel like I can optimize them for better accuracy. Overall I learned some new things and the way of teaching was really nice.

I have done two courses under Andrew ng and I am grateful to Coursera for their highly optimised and easily learning course structure. It has greatly help me gain confidence in this field. Thank you.

This course is a big part of the meat of the Deep Learning specialization. I found both lectures and exercises gave me valuable practice at grappling with the actual process of training neural nets.

Just as great as the previous course. I feel like I have a much better chance at figuring out what to do to improve the performance of a neural network and TensorFlow makes much more sense to me now.

loved it. the structure of the course, the assignments, tutorials were great!

particularly, the tensorflow tutorial was a hit!!

Cheers to Andrew who made it look much easier that I thought it would be!

Yet another excellent course by Professor Ng! Really helped me gain a detailed understanding of optimization techniques such as RMSprop and Adam, as well as the inner workings of batch normalization.

Excellent course. Bit tougher than first course. For those who have done Machine Learning course earlier and wondered that first course feels almost similar, second course is the 'real' next course.

Andrew was a great teacher, explaining complicated topics in a simple and intuitive way. The programming assignments helped to put theory into practice. A great place to start learning a new field!

I think I have a much better understanding of some of the Neural Nets hyperparameters. I can intelligently speak about the Adam optimizer now. I also got to play with Tensorflow. Really good stuff!

Very useful contents, informative and entertaining.

However, the contents in the last assignment needs to be updated. either to Tensorflow 2.0 or to Pytorch. Learning TF 1.0 is not quite useful now.

I am really grateful to the deeplearning.ai community and Coursera for providing such an amazing platform to learn and grow. undoubtedly, one of the best courses for learning deep neural networks.

Phenomenal 2nd course in the DL specialization. The implementation notebooks really drill into you how the internals of Neural network training work: the forward/backward/update/regularization etc.

There is very much piece of information on hyper parameter tuning that is difficult to self-teach.

The introduction to TensorFlow in notebook is wery well done.

I will soon continue with next course.

A further step in the various tuning possibilities, and of course the introduction to TensorFlow. Feel confident of applying different tuning techniques and playing around with optimization choices

It is always interesting to learn something new. The course helped to understand the important details of the setup. I would like more practical part for a deeper understanding of the application.