Overall, this is an excellent course, although it is not perfect. Trying to understand what is wrong when full credit is not earned for quizzes or programming assignments is sometimes "challenging". It would sometimes be useful to have more informative feedback.

Great course., excellent well to the point, Only nuisance I observed is during submitting coding assingments required multiple tries since at first time, all the code somehow does not go thru. So needed to save and restart notebook and cut& pasted again. Thank you

Great course. I really like it as it get more and more practical.

Few things might be missing from the class - it might be worth to encourage students play with algorithms a bit more. Say get back to the previous stage and add regularization to get better results.

Grate course, only criticism is that week 3 didn't thoroughly explain how batch normalization parameters (gamma and beta) get updated during gradient descent. (i.e. how to get dgamma and dbeta). It could have been an optional lecture for the mathematically savvy)

One of the most comprehensive course for people who want to learn the logic, implementation and conceptual understanding for deep learning. Programming exercises are a great starting point for learning implementation and they are perfectly made for the learners.

it is wonderful course i learned more in Deep learning and how to apply regularization

and how to optimize cost function also programming in Tensor flow

i thanks all teaching assistant for there efforts to learn us

and i recommend this course to DL beginners

I recommend everyone to go through this course if you really want to learn detail about hyperparameter tuning , optimizers and regularization used to make neural network better. It helps to open black box of Neural network and know in detail about how all works.

Some very useful insights into practical implementation and optimization of neural networks, and a very welcome introduction to TensorFlow. After coding networks in numpy you both appreciate the framework, as well as understand what it's doing behind the scenes.

Again, fantastic. Great way to explain how to tune your algorithms to improve bias and variance. Great explanation of what optimizers are used and how they function. Glad to know the nuts and bolts of the parameters usually defined in machine learning frameworks

This cause is good for me. It taught me how to tune hyperparameters and correct regularisation and optimisation to speed up the process of Machine Learning. I got some useful knowledge to utilise Tensorflow to quickly create a model and put it into the product.

This course is a must for understanding hyperparameters and their tuning and choosing the best ones for your model. Prof. Andrew explains everything very simply and precisely. This course is intended for intermediate users who have knowledge with Deep networks.

I think the most important course of the 1st 3. It tells about all the different optimizations and practical aspects of training a deep neural network. I would keep referring to its content in the future too. Thank you team for creating such a wonderful course.

This was one the best course as it has made me capable to increase the efficiency of a project as it has taught me various techniques of selection of data size ratios, tunning hyper-parameters speeding gradient checking using different techniques and many more.

I would like to thank coursera to giving me opportunity to follow Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization course. In the last few weeks I learned a lot of new theories and basics of improving deep neural networks.

Este curso ofrece una información más interesante que el primero, teniendo en cuenta que ya se saben los conceptos básicos. Hay un contenido de calidad que está bien explicado, sobre todo ayuda de cara a saber por qué se eligen los hiper parámetros en una red.

The gentle transition from NumPy based implemented deep learning functions to the Google's TensorFlow framework is so smooth and easy to comprehend. My understanding of the concepts has been solidified by the course. Thank you Andrew Ng and the Coursera team.

Extremely useful course . I highly recommend it . This course give me some helpful tips to tune hyperparameters , some optimization techniques that never heard before . The intro to Tensorflow in third weed is great . Assignment also proves to be insightful .

Very in-depth course to understand how to fine tune hyperparameters properly and which hold the greatest significance for performance. The Regularization techniques are also interesting to learn what the theory behind Adam, RMSprop, and Momentum optimizers.

The course materials are well-designed. However, I have to say this is not an easy course as I spent a lot of efforts in order to understand how to do the assignments. Overall, I strongly believe the course has taught me what I need to know about this topic!

I've learn a lot from professor Andrew Ng.He is definitely my super idol.Thanks a lot.It‘s a pretty awesome platform for me to learn from the giant.From the course of machine-learning to deep-learning,I really feel like I've made a lot of progress.Thanks.

This was an interesting and challenging course. Andrew gives good intuitions about the fundamentals of improving deep neural networks. I recommend having separate optional sections explaining the math behind some of the concepts for those who are interested

This course brought to light a lot of the more intricate topics in deep learning. Compared to my knowledge before the course, I now feel like I have a sound understanding of all the small nuts and bolts that work in a deep learning system. Loved the course.

This is one of the best courses on Coursera. Cleared a lot of concepts. Before this course, I was always thinking, what to do if I had to classify among multiple classes, but the explanation of softmax was actually very helpful in answering that question.

This was a very interesting and different course from others. I found it very helpful

for improving the NNs and the techniques taught with assignments give a well insight so as to how the problem should be dealt with.

Thank you to teachers and to Coursera.

A perfect course on Deep learning. Mathematical analysis well put forward by Andrew. I am looking forward to finish Deep Learning specialization. I would appreciate if he provides reference to textbooks to learn more about the fundamentals.

Thank you,

Vivek