While the first course in Deep Learning Specialization gives us the fundamental insight, this course shows us the practical aspects such as hyper-parameters tuning and mini-batching . I think it is essential for beginners to take this course if they actually want to apply Deep Learning to their domains.

Generally great course! But I think more tutorials about Tensorflow are needed. I suppose by using the current tutorial in this course, no one except those who have used Tensorflow before, is able to apply Tensorflow to their own work. The programming exercise is more like a

shallow experience so far.

The course was useful for me as a beginner in the deep-learning domain. The instructor was very clear and easy to understand. In order for the student to easily grasp some concepts, useful intuitions were provided. However, being a mathematics student I would have appreciated a more thorough analysis.

Very good overview of optimizer functions and hyperparameter search. I felt the Tensorflow portion could be better with more emphasis put on what the TensorFlow programming construct is as thinking in terms of computational graph and functional programming aspect could be covered as a general overview

The courses are good and impart knowledge from the experience of Dr. Andrew Ng. However, I wish they had more of "After the lecture" 1-2 question quizzes [they have such quizzes designed in course 1 of deep learning specialization].

These quizzes bring up the level of course (and learning) infinitely.

Again, A very good course , it focuses on building the concepts from fundamentals. I got very intuition of the way neural nets work.

The Jupyter notebooks are self explanatory and comprehensive. Though I think programming could have been a little tougher, could have used a fully hands on assignment.

Starting to get somewhere.. This course contains a bit more interesting topics and I did not feel it was as overlapping to the Ng Machine Lerning course as the first course in the Deep Learning specialization was. Also, finally, a programming task is done using a deep learning framework (TensorFlow)

Good material as always from Andrew Ng. There is a lot more "eyes-forward lectures" before the coursework on this one, which didn't suit my my personal learning preference as well (I prefer to get a lot of practise in when getting to grips with new concepts). Good course though, worth the effort.

I feel like the assignments involve a lot of cut and pasting of functions verbatim. It's a good start, but I'd like to go further and be challenged to solve a problem, rather than fill in the blanks. For example, try different optimizers to reach a particular learning speed on a given data set.

Hands-on projects are very easy. The only ask for copying and pasting some lines of code. In contrast, questionnaires are well designed and reprepresent the knowledge acquired. Another course drawback is that the last programming practice employs TensorFlow 1, the version 2 was released in 2019.

Everything was fine but there were some issues that was not pleasant to me. like seeing that there's problemmes with the videos and they're not corrected and instead there's an extra reading about that. Or when Tensorflow 2 has come, I was expecting that the course also introduces newer version.

The course and the instructor are amazing, but I fell behind schedule by one week and the last assignment was locked, and when attempting to reset the deadline, a message saying "something went wrong" would pop up. The help center responded very quickly and professionally, and solved the issue.

This course I believe is one of the most vital one after the first course in the specialization! Professor Ng covers all the concepts required for you to understand and master this course.

You might have to refer to additional resources to get a complete grasp of the concept post each video.

Great and easy-to-follow introduction to improving deep neural networks. If you are already familiar with vector algebra, many things are explained quite slowly. One star left out because the content has not been polished, but there are minor errors here and there with separate corrections.

Going from the Basics of Logistic regression-Neural network -regularization- hyperparameter selection and finally knowing how Tensor flow makes it all come together is just brilliant.

I feel confident as I understand the basics well before using a framework that makes it so easy to execute.

Realy recommended for those who finished the first course and/or the machine learning with Prof Andrew Ng. A good in deep exploration of different topics in regularization. An efficient introduction to TensorFlow which will put your feet on the first step of using DeepLearning platforms.

Need some improvement! I think the course is a little bit rush, especially on the 3rd week. I really like the 'test' assignments, which helps me to clear out a lot of important concepts. But the programming assignments sometimes bothers me not in the way of programming, but in the way of

Excellent! Very practical intro to tuning and improving neural networks. The four star is because I felt the programming exercises could be harder and not so much fill in the blanks. I don't think I would be able to code a adam optimizer or momentum from scratch given an empty py file.

A great course indeed! I give 4 star only because I'd have liked more lectures and programming exercises about Tensorflow, and how to train models using GPUs. Similarly, it'd have been great if Andrew explained in detail how to implement Batch normalization using computational graphs.

Great course. Thanks for making it available.

I would have enjoyed more tensorflow lectures to help understand the underlying mechanism of the platform. I suppose the intention is to provide that understanding through the assignment, but more discussion in the lecture would be nice.

Good course but it would be interesting to add some other methodologies on learning rate ("Cyclical Learning Rates for Training Neural Networks", "Snapshot ensembles") and some explanations on categorical variables and embeddings matrix ("Entity Embeddings of Categorical Variables")

Great course giving insight on how to fine tune deep neural networks. I believe the contents need to be a bit polished but that's totally understandable given its early stage. The comments in the discussion group will for sure help to fix some typos and make this course even better.

This course helps me a lot in tuning hyperparameters in training machine learning model, just one issue is the last programming exercise when using framework the guide is missing something, which is hard for some to complete, for me, I have to use Google Search to find the solution

Need Information about other parameters like #of iterations, how to choose number of hidden layers?, number of neurons in hidden layers, inclusion of few other strategies to choose neural network model will be helpful. If they are covered in next courses, then please ignore.

Thanks

A useful class delving into the nuts and bolts of building a reliable nn. Well structured and explained. I feel like the use of Jupyter in the homework makes it simpler than it should be. A large portion of the code is already written and the instructions often give up the answer.