Very detail derivations and directly explain many concerns I have about deep learning. A excellent part is the programming tips to develop deep learning algorithms

very detailed materials and lectures are easy to understood. I would recommend study with the cs230 from Stanford which is the more advanced version of this course

very good course. the programming assignment is actually teaching me how to do the programming, not only focusing on grading my result. excellent lecturer, thanks!

I really enjoyed how simple the explanations were and the programming assignments helped me understand the material better. Really helpful for any interviews also.

Terrific course! Andrew Ng is a great lecturer, making concepts clear and concise. The notation in this course is also vastly improved over the original ML course.

It is an excellent course for start with neural networks and deep learning. And it also helps better understand machine learning concepts like logistic regression.

A nice hands-on introduction to neural networks and first steps into deep learning. I liked that very much and look forward to what comes in the subsequent course.

I went from having a good theoretical intuition about how deep networks should work to direct knowledge about how to implement one. Amazing class, thank you!! :)

A great introduction to deep learning, I would have liked more mathematics involved, but I understand why it was necessary to cut back on math. Highly recommended!

Phenomenal course by Professor Andrew Ng. The concepts are throughly explained and the assignments are extremely engaging.

Thanks for offering this specialization!

I would like to thanks Andrew and all people involved in this course for all the effort they put in this course. I have learned a lot and had great time as well.

The explanation builds up over time, that's what I love about this course. I was frightened of this very topic, because other courses didn't explain it like this.

This is an excellent course. Thanks to Andrew NG sir for providing us a beautiful content. I learned a lot, and I will learn a lot more in this domain. Thank you.

This course addressed all the issues that Machine Learning course had. This course is easier, more interactive and had a much more consistent learning experience.

Clear and well structured. One observation: a reminder on matrix calculus, the same kind that the one on derivatives, would be nice before heading into equations.

This course gives me good understanding about the math logic behind the code. It will help when designing new algorithm, Mainly while troubleshooting an Algorithm

Thanks to the Coursera team for providing such a nice environment to learn easily, though I don't have a good GPU the online lab activity is really appreciatable.

Very interesting course, I really appreciate how much assistance is provided in the programming assignments themselves because I struggle a lot with writing code.

This course is a really good introduction to neural networks and I have to say that programming assessment are very exciting and motivates me to continue learning

I am not new to deep learning. I have done it without knowing the inner details well. I felt confused what to do if my model failed. This course is an eye opener.

Wonderful course. Thanks to all Coursera team and deeplearning.ai staff. Very grateful of your well done job and for giving this opportunity to worldwide students

Very comprehensive and informative introduction to deep learning. Everything is step-by-step and clear, from the math to the explanations and the coding examples.

It is an amazing experience to be able to follow Andrew to get a comprehensive understanding on deep nn network and implement the fp and bp yourself with python.

Очень качественный вводный курс. Даже человек без математической подготовки сможет его пройти и понять основы построения нейронных сетей. Спасибо авторам за курс!

This course has great content and i prefer every cs engineer if they are looking for neural network and deep learning course ,the this is the best course for it .