I suppose Hyperparameter tuning, Regularization and Optimization are some of the most important aspects of Deep Learning, since 90% of most of the DL projects come down to just that. Andrew masterfully dives into the intuitions behind some of the most widely used approaches, and the programing assignments are designed to show the impact good tuning could have on a DL algorithm.

Great explanation of optimizations that can help speed up deep learning algorithms. Loved the little tips and tricks that are covered in different sections. The easy with which Prof. Ng explains complex concepts and analogies is commendable. The programming assignments are very helpful to people without expert programming experience too, that makes the experience very smooth.

An Excelent course on learning how to tune hyperparameters and also a peek in using TensorFlow (in the last week, and trust me, it's worth the wating) it's complete, educational and the professor Andrew Ng it's an excelent teacher! I would have liked some more practice on parameter tuning in the programming excercises. But in a nutshell it was a awesome learning experience!

In my opinion it would be a good to have a short video describing how to drive the ml project in the company. As i am taking ml course and this specialization, I started with working on octave, then numpy then tensorflow, so it would be good to have some advice/tips on when to use octave or numpy or tensorflow for building a model when you get a project in ml in your job.

Post the first course, this course would is the one that is going to make you feel like a deep learning practitioner. You get to understand why deep learning is sometimes called an art how much difference in terms of speed and accuracy can be made just by tuning the hyper parameters. Highly recommended if you know deep neural networks and willing to dive deeper into them.

Again a great course about deep learning. The course structure is very well defined, with step by step to build technical foundations in the beginning and later using open source deep learning framework to connect all the pieces together. Dr. Andrew Ng made all of them very easy to learn and sometimes I feel like I should jump out the comfortable zone he created for us.

Very nice course about important subjects of Vanilla Neural Networks, as optimizations algorithms , regularization methods, hyper-parameters used and how to implement them in practice. A very nice chapter on the sequence of the specialization that give me understanding on important aspects of it, how to use and how to implement them. I really enjoyed each detail of it.

This was a really challenging course, with lot's of beneficial tips, very well prepared and delivered, I do suggest to send us best practice assignment code after passing each assignment, to be more confident moving forward in the Deep-learning specialization, finally I am really grateful for the effort you are putting behind the Deep-learning specialization courses .

This course gives a very good idea of the overfitting problem in deep learning and different ways to overcome it. It also introduces commonly used optimization methods in deep learning. A nice introduction to tensorflow is provided in the last week's programming assignment. Overall it is a very satisfying course. Many thanks to the instructor and the entire team!

Key course in the specialization and covers wide array of topics which are responsible for improving the DNNs. Complicated than the first course but very well explained by Andrew Ng. Things definitely get clear after doing the programming assignments. One should definitely complete this course if one has already completed the first course. I totally recommend it.

Course was amazing, content was amazing, assignments was amazing.

Andrew Ng is the best teacher I have ever experienced in my life. I learned a lot from this course, these things are very difficult to learn from research papers it takes a lot of time but person with great passion of deep learning can learn all these things in just three weeks. Highly Recommended.

In era of deep learning bloom, know how to control network model is an important thing. And this course has them all, from tuning learning rate to speed-up convergence or applying drop-out for avoiding overfit, etc... It shows you the under-the-hood theory and brings you the knowledge to grasp the basics yourself, and actually can apply back into your projects.

The course is very useful for being acquainted with tuning hyper-parameters and modern optimization algorithms like momentum, RMSProp an Adam. It is also introducing how to prevent over-fitting efficiently from recent papers in addition to mini batching training data. Although it introduces TensorFlow in a brief way, the overall assessment needs some revision.

This course was as the first one. Nothing new, extremely interesting with Prof. Andrew. All assignments are such amazing. I like the way how they are formatted. It gives us a golden chance to revise theory and apply it immediately. Everything was just right. Thank you Andrew and TAs and all people who helped making this course looks the way what it looks now.

Gives you an in-depth understanding on how to finetune your neural network hyperparameters and introduces you to the various optimization methods. Finally, an introduction to TensorFlow gives a more practical solution to developing your code fast and easy. Yet again, Andrew Ng is nothing short of brilliant and his ML content is always the best in the world.

I strongly recommend this course since I pass an interview after finish the first and second specialization. Although it is not enough for some high-demanded company, it is a really good lecture and experience for the new beginner in neural networks. But I have to say that the project is too easy so far, I wish we will have more great exercises and projects!

This course trully go deeper into the deep learning and I learned a lot of things which improve my concept about NN network. Andrew gave an excellent lesson like the first course and simplify everything and the quote from Andrew "if you dont understand anythink don't worry too much about it" really make sense and over the time the concept will get clearer.

Very clear, straight to the point, explanations with very well guided programming assignments in Python to hammer the concepts. A lot of knowledge and experience condensed in just a few hours and materials. I recommend previous exposure to Python and Machine Learning to make the most of this course (Ng's Coursera's course provides a very solid foundation)

This is an amazing course , the way they had designed the transition from numpy to tensorflow was amazing. The the concepts of gradient descent with momentum to adam optimizer was great coming from your previous course , I can't express how much this has grounded my understanding. I'm pushing myself to complete the specialization. Thanks a lot everyone !

The course if very structured. Can't think of any improvement in course structure. Will like to thank Andrew Sir for this great effort.

As an improvement it would be great if people can be encouraged to solve problems on different dataset on internet such as kaggle. Such sources with other help can be provided as work to do after the completion of Kaggle.

A fine continuing of the previous course in this specialization.

Learning optimization algorithms to improve our parameters' update, how to normalize the inputs at each and every layer, how to prioritize certain hyperparameters over others when testing.

All culminating with Tensorflow, a platform that saves us a lot of time in programming Neural Networks.

This one got pretty much quicker than the last course and was quite simple, though valuable inputs were provided by Andrew Ng. He is the coolest DL teacher i could have come across till now. Tensorflow is awesome and good basic idea was given in this course. Suggest it for everyone to go through. and I am running towards finishing this specialization.

Brilliant material altogether.. almost a compulsory course for researchers diving on the ocean of deep learning.. While I was reading papers on deep learning I came across all these terms but couldn't understand it.. Now the picture is pretty clear... Thanks Prof. Andrew Ng for this wonderful effort. I have already recommended this course to everyone.

A great course talks about the detail in building Neural networks. With the first course as a foundation, student taking this definitely will get a better understanding on hyperparameter tuning and optimization, in addition on training neural networks. I recommend this course to those who would like to know neural networks more than just the concept!!

The details covered in the course are very important for pracical use. They are not commonly available on the Internet otherwise. Also, with the new libraries that make the task of coding easier, the knowledge of tuning parameters, of optimizing learning curves, is often overlooked. This course highlights the importance of that knowledge. Thank you!