NIce Course on hyperparameters search and tuning. The optimization functions and its relation to the hyperparameters is well taught. Mini-bacth normalization during training and application of learned parameters in testing is discussed very well. At last, deep learning frameworks were introduced and the practical training on tensorflow framework was awesome. Thaks for the well designed content.

The course covers the theory and implementation details of advanced optimization algorithms. A good amount of intuition was provided in the explanation of these algorithms. A basic explanation of bias and variance and how hyper parameters affect them both is explained clearly. I liked the hands on part, as it allowed me to implement the algorithms discussed and gain more clarity in the process.

Very useful follow up to the first course in this specialization. Learned all the details of how to tune and optimize a deep neural network, as well as nice introduction to Tensorflow. Some typos in the comments of the final assignments but they were easy to spot. This time Jupiter notebooks worked better that during the time I was working on the previous course with less or no resets required.

Very useful and detailed !! And also let's not forget the things I love most about this series , Andrew's dedication in explaining new concept and code , and the design of the practice assignment ( the coding lab ) . The lab is very very helpful !! The non-relevant code are already typed in by the author , so that we can focus on the main code that supports us in understanding the new concept

Another excellent course. It provides a good background for understanding more about neural networks with a reasonable amount of time and effort. I have no illusion that it is providing knowledge in depth, but I have a much better knowledge of the basic terms and concepts that I did before. I am pleased to know at least something about tensorflow and how to use it to build neural networks.

Excellent Course. Really structured way of learning the importance of hyper parameters and their effects on the learning/training and hammering concepts like "regularization" home.

Just an observations, but it seems like the mentors are not that engaged in these courses anymore, but there are enough help threads that one can figure out the questions - specifically on the programming exercises.

What an amazing course it is. Perfect explanation how we can use optimize our cost more efficiently and effectively. Also this course includes techniques to overcome problems like over fitting i.e Regularization and Dropout techniques. Information about Batch Normalization is very splendid. Also got little intuition about tensor flow. Thank You Andrew Ng for providing such a wonderful course.

Prof Ng is a great teacher and is good at making the difficult material very easy to learn. I am very interested in the DL. Before I took this class, I found that since this field is very new so all the material you can find is a little piece and not systematical. This specialization is a wonderful and systematical, easy to learn and fun. Thanks for the great work those teacher have done .

Awesome illustration on deep network's regularization techniques, weight initialization techniques and gradient checking, and more. This class provides you with hands-on experience with how to tune a deep network efficiently. You will not only learn the techniques but also understand many of the intuitions of how each technique works. A must take if you are dedicated into machine learning!

Comencé esta especialización sin conocimientos de deeplearning en absoluto, hoy habiendo finalizado la especialización tengo una basta noción de este mundo tan apasionante. Quiero destacar la facilidad con la que Andrew transmite su conocimiento, es un instructor de otro mundo!. Feliz de haber realizado la especialización y de continuar por este camino. Gracias a Andrew Ng. y a Coursera.

This has been a very useful course and helps you to understand much more about neural networks including regularization, optimization algorithms, hyper parameter tuning and programming frameworks. The style of teaching and the programming assignments are of a really good standard. The quizes could be improved to be a bit more challenging but they still help to review content quite well.

I learned a lot about the area that is not much talked about in deep learning, which is hyperparameter tuning! The forum was very helpful in debugging the programming assignments! Thank you Prof. Ng for the wonderful course. I thank Coursera as well for believing in me and granting me Financial Aid. It wouldn't have been possible without your help, Coursera Team. THANK YOU VERY MUCH! :D

This was an excellent follow-up of the first course. Having used adam optimization for almost all the neural network models I have build it was great to understand the mathematical intuition behind adam optimizers. Also the programming assignment gave a wonderful refresher and practice of tensorflow. Overall I'm glad hyperparameter tuning and optimization was chosen as a seperate course

The course is great and will help you in understanding on how to optimize your deep learning algorithm and tune your hyper-parameters. The course provides insights into the exponentially weighted averages concept too which helps you understand how things work behind the scenes when trying to optimize your algorithm. Dropout and regularization have also been explained to a good extent.

This course is very useful for practical purpose. I've learnt a systematic method to develop and iterate my algorithms, which saves me a lot of time. And it's been the first time that I get to know so many variants of gradient descent method, such as Adam and RMSprop. By the way, the programming assignments get a bit hard, but it help me better understand the algorithms. Thanks a lot!

This was a useful course for newbies in neural networks. It gave useful hints regarding how to update the model one is using based on what problems one observes, as well as how to tune the hyperparameters (if there is enough computational power or one runs a small problem). Obviously, this is just a starting point and one should invest a lot of time and energy to become experienced.

The course 2 of the DL Specialization surpassed my expectations. With its practical approach and expert instruction, I gained a deeper understanding of hyperparameter tuning and Batch Normalization. The hands-on assignments were instrumental in reinforcing key concepts and honing my skills. I highly recommend this course to anyone seeking to advance their knowledge in deep learning

All the quality of the first course, but even better. My 4-stars for course one were addressed in these Jupyter notebooks. They were still manageable but the prompts provided very good reinforcement to the various tuning algorithms. A top-notch offering...one I'll be sure to recommend broadly. I'm very much looking forward to the remaining courses in the Specialization. Thanks!

I had a very good time getting teaching sessions from ANDREW NG .., I am a second year student and have entered in this field of deep learning since some months then i encountered this specialization and with the deep concepts of Sir ANDREW NG ,i am now able to make much more complicated models ever before...I hope i could get an autograph from my Ideal in this field

Mr.Andrew Ng

This is a great course to learn about practical aspects of neural networks. Some parts are challenging to consume as most of the material relies on intuition rather than detailed mathematical explanation. This helps to involve more people in the course who are intimidated by mathematical equations. A great addition would be to have optional mathematical details in separate videos.

I've learnt much from course including preprocessing (mini-batch, regularization, normalization), gradient descent algorithm (batch gradient descent, stochastic gradient descent, mini-batch gradient descent) and the variants (momentum, RMSProp, Adam). Also there's TensorFlow tutorials which I love best.

Thanks for Andrew Ng for bringing us such an amazing fundamental course of DNN!

Very well structured and thorough course. Instructor did a very good job in teaching the topics of NN such as Regularization, Optimization etc. and explaining the mathematical concepts such as moving average.

Lab made coding assignments easy to understand and code. Lab made it easy for me to understand the structure of NN and how to code clean NN functions for easy implementation.

Excellent! This was number two in the series and both courses so far have been excellent. That notation and frameworks are consistent and build nicely. The exercises, quizes and programming assignments really reinforce the material.

This course is true to its title and one you should definitely take if you want to understand a lot of the nuances of constructing a deep neural net.

This course really helped me getting the deep insight into the hyper-parameters which need to be tuned to get the optimal learning of the algorithm with the different algorithms necessary for improving learning rate.Andrew Ng really simplified the tough things and arranged them in a proper series of videos that is easy to understand.This will really help me lot in future.Cheers!

Great course, but please have the TA proofread the subtitles (the auto-generated ones are bad, if I was deaf, I would have no chance to understand anything) and upgrade to at least 1080p video. After all, since this course costs tuition, there should be a minimum technical standard guaranteed.

But content wise and teaching style wise this is fantastic, thank you so much, Andrew!