Great course!!! The best online course I have ever taken! I enjoyed almost every day I participated in that course, really an educational treasure! It is so comprehensive and detailed at the same time. Due to the good presentation of the topics it was really understandable.

Very conceptual learning related to deep learning. Really appreciate the mentor who is teaching from the depth and able to clear all the concepts related to course topics. Highly recommend to those who haven't gone through all the videos and programming assignments.Thanks!

I have really learned a lot of things! It surely took 3 weeks to complete all the things, it was tough at some points, but if I didn't do this course, I might have some regrets that I didn't achieve all the knowledge. Thanks to Mr. Andrew, he is really a very good teacher.

Wonderful course and material. Andrew has a great way of explaining the topics in the simplest way. Although I had some issue with understanding the optimizers, I learned a great deal. However, This course needs a revamp using Tensorflow 2.0 for the tutorials :) Thank you

there is a lot of materiel that is being discussed during the lectures, and all of it seems like it could be really relevant. I am missing a consolidated course deck - ie something like a deck of slides on all the important concepts that are being discussed, for reference.

Another awesome course by Andrew. I wish he was my professor in my grad school. I hope Coursera publishes all the notebooks + data on public github so I can redo all the exercise again. Too much info to digest in short amount of time. I can't wait for RNN and CNN courses.

Excellent course. A great way to understand the fundamentals. It's always good to understand what's under the hood as frameworks abstract away a lot of the hard work going on underneath. Also makes you aware of how to be better tune and understand hyperparameters etc.

Easily the best course on diving under the hood of how a Neural Network actually works and how to tune to the satisfaction of our results.

A no brainer for sure. The best part the exercises. You MUST do the exercises to understand thoroughly how the systems actually work.

I like this course, it details basic while popular technique we need to optimize neural networks. also the lectures on different optimization algorithms are very helpful for you to know details on how they run when we choose these algorithm in frameworks like tensorflow.

I found this course extremely helpful. It enabled me to develop a really good intuition about how deep learning models are made, and what are the small steps that go a long way in improving the overall performance of the system. I hope all of you find this helpful too.

Excellent course, giving a very good insight into how to approach building a deep neural network, the concepts of various parameters, tips on how to best achieve a good algorithm and a step by step walk through of the different algorithms, parameters and optimization.

Quite detailed curriculum. It is a great continuation for course 1 of this specialization series. As usual, Prof. Andrew Ng is there to guide our way throughout the course duration. A really fun and intriguing course which can lead to course 3 as a proper continuation.

This course was perfect for me. I thought it was a good balance between theory and practice. I don't think I'm ready to start building NN's from scratch, but at least now I know how to get started. Also, I now have an understanding of the complexity of a ML project.

Mini Batch/Adam Optimization concepts was very well explained. I was expecting the detailed derivation of the backpropagation for the batch normalization case. Overall it was a great course and it greatly improved my understanding about concepts used in deep learning.

4.5/5 A diferencia del primer curso que es una continuacion del de Machine Learning de Andrew Ng , aqui vemos una evolución del contenido , se pasa a ver miniBatch Gradient Descent, Regularizacion , Momentum , Adam , y un inicio a tensorflow

realmente un MUY BUEN Curso

Through the class, the tuning of Hyperparameter is detailed introduced and more important is that why it's tuned is very clear. Suggest persons study deep learning to study this class carefully.

Expect to have more info from the current study in University or College.

I guess the difficulty is what you make of it, with further studying and dedication, but I would like to encounter more challenging assignments, where one has to code entire cells for instance, as opposed to a single line here and there.

But everything else is great!

Contains very good understanding of Hyperparameters and their tuning process.

Secondly, teaches very well the mathematics of optimizers such as GD, SGD, GD with Momentum, GD with RMSProp and ADAM.

Finally, a small glimpse of Batch Normalization.

Highly Recommended!!!!!!

I had previously used optimizers with momentum and variance momentum (Adam) with the understanding that they helped without knowing exactly how. This course cleared up all those tiny details and has left with with a greater appreciation of neural networks in general.

This was a great course. I could develop solid intuition about how neural networks work, and learn about state-of-the-art ways to make them better. The only thing that I have to complain about is the fact that the Tensorflow part should be updated to Tensorflow 2.0.

This course broadened my understanding of what really happens when driving the cost function closer to its minimum and techniques to go there faster. I found this course instructive and the programming excercises helped a lot to digest the learnings from the videos.

I know deep learning already, but I saw many people who even know it doing this specialization,so i too started like that..but its a very good experience concepts are very well explaining and I am enjoying assignments a lot it a very fun experience doing all again..

I find this course very useful, many complex ideas are presented in a very understandable way! This course is like a collection of all important aspects! However, homework could be more difficult, because now almost all the answers are given in the python notebooks.

This specialisation is an exciting journey - can't wait to start the next course. The foundational concepts of neural networks are expertly packaged in these courses, together with enough practical exposure to get you started on a fun learning and career experience.

Best course to improve your understanding of Neural Network tuning, moreover the Tensorflow course at the end of 3rd week is really detailed, I worked earlier with tensorflow but didnt get its details accurately, but now I am confident enough about using tensorflow