I really like the practical aspects of the course where although there is a focus on teaching the fundamentals, there is also a good focus on teaching the latest frameworks to apply the knowledge of the learnt concepts more efficiently.

Excellent practical advice on running NNs. The lectures teach the subject matter in a lucid and intuitive way. The course ends with a TemperFlow exercise that shows how to implement NNs at a higher level than peviously shown with numpy.

Great material. Short, precise videos.

It would be great if you propose projects to work on outside the course, in order to learn more about the topics. Just like ideas where we could apply what we have learned and a seed to build upon.

Loved the programming assignments. After learning Tensor flow in this course, I learnt about Keras on my own. It made model building very easy, but without understanding the basics, going straight to Keras would make a person dangerous.

Again, the course was great. Covering a large spectrum of deep neural net adaptations and configurations of its hyperparameters give me a better understanding and tips with how to best use this deep learning technology. Congratulations!

Very good and interesting course!

Programming assignments were a bit easy, but it does not bother me as this is not an "introduction to programming" course. The point is to get the basic ideas of programming these kinds of applications.

Absolutely loved the course. have learnt the basic pillars of Neural Networks and DNN. Andrew has clearly explained the diagnosis of a problem and identify bias and variance. then Regularisation techniques, Optimisation algorithms etc.

Thanks to this course I finally learned to optimize a neural network through the tuning of parameters and hyperparameters. And then I finally had my first experience with Tensorflow.

Absolutely recommended. Andrew never disappoints me.

I can't emphasize how much I enjoyed this course. The course material is clear, structured and well laid out and each concept builds on the previous with repeated emphasis on key walk away points. Can't wait to start the next course :)

This is a great course like other ones in this specialization. I learned from this course why we need regularization, how to do them exactly, what are the rules-of-thumb for setting hyperparameters, and how to find them systematically.

After learning neural network and deep learning it is important to learn improving networks.This course gives idea to improve your network.Only knowing how to build a neural net is not okay you should also know to improve the network.

Had a lot of confusions on why and how to tune hyper parameters. Got a good amount of knowledge in Mini batch, batch normalization, momentum, Adam and RMS prop. Will surely be useful when I tune hyper parameters in my future projects.

Great course. It was a little short, but covered the necessary parts of hyperparameter tuning. I also liked how the last homework was done using TensorFlow and how the courses in the specialization build upon the preceding lectures.

Teaches "what it is" and "how to do it". Clear steps, easy to follow. It would be great if you can also teach "why it is like this", or say, why is regularization valid, what is the theoretical justification behind regularization etc.

Excellently taught by Andrew Ng. While the field of Deep Learning and AI continues to evolve rapidly, Andrew maintains calm and explains the core and relevant aspects needed to succeed in this course and hopefully also in your career.

I learned a lot of techniques which I can apply in improving my deep learning projects. Very happy to have selected this specialization course. Andrew Ng's style of teaching and imparting a complex topic with examples is unmatchable.

Very clear and concise explanations. The advice from the instructor's personal experience is particularly exciting. It provides guidance and assures you that you are on the right part. This course is great to help develop intuitions.

Another great course which is an essential companion to the first course so that you know different techniques for improving and troubleshooting your neural networks. The 3rd week exercise also contains a nice tutorial to TensorFlow!

As with the first course of this specialization, Professor takes one step at a time building/ explaining things. He explains even minor details, so it very easy to understand. Also the assignments are very useful to learn the topics.

Very important course in the path of specializing in deep neural network. The working of optimization and Regularization algorithms help you understand the way to improve the deep neural network thorough tuning the hyper parameters.

It has been a very helpful course for me. I got a proper intuition behind the hyperparameter tuning because of this course. Professor Andrew Ng's pedagogy and coursework design is just perfect and i really enjoyed learning from him.

Very Important topic in ML projects. The course gives the intuition for parameters of neural networks and how to choose them. Although slow pace with only a rough idea about each parameter but It is highly recommended for beginners.

Excellent course. Andrew Ng is an excellent instructor, providing very intuitive explanations to very complex models, and very useful applied advice. Makes the course super accessible to anyone with a basic background in statistics.

Awesome course as always ! It is very nice to have a very experienced deep learning practitioner showing you step by step how to build your own models and sharing many tips, intuitions and practices in a such a fluid and clear way.

Best course from deep learning ai i have learn regularization , different optimization algorithm like rms_prop, momentum and Adam also lean what is deep leaning framewrok and also use one of them in assignment of week 3 of coursera