Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization

After completion of this course I know which values to look at if my ML model is not performing up to the task. It is a detailed but not too complicated course to understand the parameters used by ML.

I really enjoyed this course. Many details are given here that are crucial to gain experience and tips on things that looks easy at first sight but are important for a faster ML project implementation

Thank you Andrew!! I know start to use Tensorflow, however, this tool is not well for a research goal. Maybe, pytorch could be considered in the future!! And let us know how to use pytorch in Windows.

Exceptional Course, the Hyper parameters explanations are excellent every tip and advice provided help me so much to build better models, I also really liked the introduction of Tensor Flow\n\nThanks.

very useful course, especially the last tensorflow assignment. the only reason i gave 4 stars is due to the lack of practice on batchnorm, which i believe is one of the most usefule techniques lately.

Assignment in week 2 could not tell the difference between 'a-=b' and 'a=a-b' and marked the former as incorrect even though they are the same and gave the same output. Other than that, a great course

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.

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.

Excellent content. The grader seriously needs to be updated thogh. For example, it needs to be Python2 and Tensorflow2 compatible and also needs to be robust in handling common syntaxes such as "-=".

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.

Fantastic course! For the first time, I now have a better intuition for optimizing and tuning hyperparameters used for deep neural networks.I got motivated to learn more after completing this course.

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.

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.

Would have liked to see the math and more complete explanations for all the things that Prof. Ng glosses over by saying "you don't really need to understand XYZ". Even if this material was optional.

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.

Having a good understanding of tuning the Hyperparameters is key to build powerful neural networks.\n\nThe course helped me to keep a focus on tuning and understanding the relationships parameters.

very good course with deep knowledge of each parameters. Little bit stretched at tensorflow. A bit of overview on tensorflow API and tensorflow architecture could have been better before exercises.

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

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.

Thank you! Great lecture videos and programming assignments with a lot of help built in. I was able to figure them out. Need more practice to master the materials. Thank you! This is a great start