Very useful course which helped me to organize my thoughts about how to select the model hyperparameters, how to extended the network for multiple outputs using the softmax function. Additionally the Tensorflow lab was very helpful as jump start.

After Completing this course I can now understand some of the secrets of Regularization, Batch Normalization, Learning Rate and many more thing related to Deep Learning.Assignments are very relatable to explore what you just learn in every week.

Legendary Andrew Ng strikes again! Great chapter of this Deep Learning Specialisation! Good balance of theory and must-know concepts for improving neural networks! I especially appreciated the optimization part and the tensorflow coding exercise

After completion of this course, I knew what I have to do to make my neural network to perform better and how to avoid over-fitting. And what range of values and techniques, I can use/check and how I will set my hyper-parameter more effectively.

This is awesome.

Andre Ng gives great lectures, the practical exams are easy to follow and no installation of software and other annoying bumps on the road as everything you need is running smoothly using Jupyer notebooks on preinstalled servers!

This was the best course of this series as I have observed it was awesome to learn various normalization techniques and then implement them form the scratch and then the various optimizer. But in the last programming exercise the task was less.

A important aspect of machine learning that is left untaught and ignored that is hyperparameter and optimization.This course cover them all and also the best practices .Deeplearning.ai specialization is really the best course I have ever taken.

Contents are structured in a way that it is so easy to pick up. Programming exercises are exactly in the balance that is not too hard or it isn't too easy. Learned a lot in this course especially. Thank you so much for this excellent course !!!

A good summary of Hyperparameter tuning, Regularization and Optimization. One suggestion would be to extend this course for 1-2 weeks and emphasize more on the intuitions and mathematics behind the optimizers. But again, the videos are awesome.

Simple and straight forward explanation of most helpful tools. Examples are well designed. However I feel that introduction to TensorFlow is very brief, it should have been more elaborative. Anyhow overall a well designed and well paced course.

This course taught me a lot of things that I cannot usually find in a school curriculum, yet the content are extremely useful in helping me to accelerate my algorithms. This course is super important in handling deep learning projects, I think.

Another excellent course by Proffesor Ng. Lots of material to learn but neatly organized and extends the journey from the previous course. Lots of ML/DL terminology is clarified and their place and importance shown in this course. Very useful!!

Take this course if you want to know:

1). He Initialization

2). Adam Optimization

3). Batch Normalization

4). Softmax Activation (or regression)

5). Tensor-Flow for programming Deep-net.

It is an amazing course. It is well-structured and presented.

Great course, learned a lot about hyperparamter tuning, different types of optimization methods, and regularization techniques. But, I felt more Tensorflow exercises are needed to get good grasp and use it smoothly to solve different problems.

This is just an amazing Courese in Deep Learning, about neural network optimisation by chosing optimum hyperparameter. I would recommend every boy who i really interested in learning the deep thought of deep learning to go through this course.

Understand well the fundamental and most of the hyper-parameters tuning mechanism and processes. The course has included some fundamental knowledge of using TensorFlow. It is well organized in the teaching processes and programming assignment.

Great Course. I especially like how the mathematics of Deep NNs are presented, taught and practiced, before the revelation of frameworks such as Tensorflow(in particular). This gives me a great understanding of what these frameworks are doing.

This course brings you all the details to make your neural network training works better (faster, more accurate, etc.). The materials are presented nicely (as usual), not only the formulas but also the intuition behind them as well. Very good.

Really cool course, review what i knew already. In addition learn more common optimization algos (GD with momentum, RMSprop, Adam). Get the sense of approaching the most tricky part hyperparameters tuning. Nice and smooth intro to Tensorflow.

This course has been amazing. I have learning various interesting topics such as overfitting,techniques to avoid overfitting,Different optimizers and softmax regularization. Tensorflow framework is awesome. Looking forward to learn that too.

Great course, Andrew Ng is just the best teacher for NN and machine learning. Assignments and quiz too easy, probably i will struggle on the implementing it on my own. But is common to MOOC so definitely the best deep learning MOOC out there

I wish these assignments would never get completed. Very nicely hand holding done through out the assignments so that you don't loose interest and also teach the intended stuff without watering down.

Thanks a lot! Recommend this course highly!

I'm a beginner in ML so i don't know much to say but this course teaches what it says it will do. In the middle of the course you may be having lot of questions about how it all fits together, but they almost always get clarified in the end.

Dear Committee of Coursera,

I wanted to take a moment to express my gratitude for the incredible support I received throughout my learning journey.

The support that I received from Coursera was commendable and I just want to say: Thank you

A LOT OF INTERESTING CONTENT

A lot of content to improve the knowledge about NN and related frameworks! As always a lot of guided interesting practices very applicable to real problems. I will continue with the rest of modules without doubt.