I would like to thank everybody contributed in this course, surely the amount of effort they put in all aspects of the course made it clear and easy to understand. Andrew Ng really has great ability to explain things clearly and simply complex formulas/processes, I liked that he explained the intuition behind almost everything thing discussed in this topic. I also like the programming assignments and how they are beautifully structured. Thank you very much for all the things I learned in this course.

Awesome Course for Diving into the World of Deep Learning and AI. ANDREW NG Sir Explains the Concepts of Neural Networks in such an Excellent Way so that they are Understood Easily and also in Depth. Also, the Programming Assignments are Well Designed so that you can Understand the Concepts Deeply and Practically Apply them in Python. A little notion of Machine Learning is required to make more sense but you will still understand the concepts.

Huge Thanks to Him for Creating such a Great Platform!!!

The whole course had an excellent pace and covered all the vital topics in great detail. Being an engineer myself it was easy to grasp the principles of forward and backward propagation, the chain rule of differentiation. Using python program was also a great plus. Though I have some programming experience I had never sued python before. Lastly I would like to Professor Andrew NG. His sounds so cool and peaceful, and puts the students in relaxed mode, ,thus improving the learning experience manyfolds

I am software engineer looking to expand my skill set to cover Deep Learning. I first learned that Andrew NG was a big reference on AI when I read Life 3.0. Then I searched about him and found he has a DL course on coursera and so I didn't even hesitated. This is my first course in Coursera. I found the classes super smooth to follow as Andrew NG introduces the topics in a very easy to understand way. I am super excited to cover the next courses. Thank you so much for sharing your knowledge this way!

Beginner friendly course. This is Andrew's Ng first but very important course and that is prerequisite of next courses of same specialization. Assignments are well designed by instructor very helpful to understand the

theoretical material. Assignments designed according to real world problems like image classification. Well effort by instructor that makes easy all the difficult topics for us and thanks to coursera team that providing us such a great platform where we learn something new at any time.

I am extremely satisfied with this first course of the specialization. I think it is a rigorous course, which provides all the key concepts, driving you to go deeper into the mathematical issues. I think it is the best MOOC I have taken so far.

Estoy sumamente satisfecho con este primer curso de la especialización. Creo que es un curso riguroso, el cual provee de todos los conceptos claves, impulsándote a profundizar en los aspectos matemáticos. Creo que es el mejor MOOC que he realizada hasta ahora.

Professor Andrew Ng's teaching style is simply amazing! I was able to absorb the material fairly quickly and reinforce my learning with very well structured exercises. I, now, have the confidence that Deep Learning is no rocket science. It is pure mathematics and art at play! If your algebra fundamentals are in place and you are creative, there is no better path to AI than Deep Learning. Believe me, when you start "getting" DL concepts, it quickly grows on you and you are addicted to its philosophy!

So happy I completed the first course in the series of Deep Learning. I got a great foundation for how neural networks work, with good instruction, good illustrations, and plenty of resources. The lab notebooks are particularly well-written, with thoughtful instruction and step-by-step application of what we learn each week. Outputs have "expected" outputs shown below, so you know if you're on the right track or not. Overall very happy with this course. It's a good bit of work, but so worth it.

Learnt about logistic regression and forward and backward propagation along with different activation functions of neural network and its implementation with python programming . The hand-on projects was very very helpful to get a better understanding of logistic regression and neural network implementations . also helped me learn the working of Jupyter notebook (i wasn't aware of it , so an extra thanks). in short a very very helpful course and the programming problems adding on to its greatness.

An excellent that covers the fundamental required for deep learning. Professor Andrew Ng gives an excellent intuition behind the inner workings of deep learning and practical guides for implementation with the help of the assignments. I found the heroes of machine learning section to be the icing on the cake as it gave a broad overview of the latest developments in the field of deep learning. To anyone who wants to get an insight into this wonderful domain, I would definitely recommend this course.

Terrific intro to neural networks! The instruction was very clear on the steps that made up NN/DL algorithms and very easy to follow. I really liked how the programming examples were explicit in what made up the algorithms, and then there were test cases for each section of the code. This made it easy to step-debug through the code, rather than waiting until the code is complete and running into a bug and having to try and trace back through the entire notebook. Thanks for putting this together.

Great topic, well organized, and very understandable. Tests and assignments are structured very well and are completely doable.

I get the dynamic aspect of writing the lecture notes in the videos, however the lecture notes should be "cleaned up" in the downloadable files (i.e., typos corrected and typed up). Additionally, the notes written in the video could be written and organized more clearly (e.g., uniform directional flow across the page/screen rather than randomly fit wherever on the page.

Although there are many pre-written codes, I think this course gave a good and easy image how neural net is confirmed and works to a beginner.

Some more things I also wanted are explanations or texts for how to prepare datasets (image data, in this case), and some other usages, not just distinguishing images but sounds or texts and so on too.

But maybe image is most easy example for a person who really don't know well about math or program. I still want to get next courses for further study.

Extremely helpful course. I got a good and depth knowledge about the Neural Network, Activation Function, Vector and Matrices, Forward and Backward propagation, Parameters and Notation. The main thing I love with this course is the implementation of theory and examples practically on the code. While you are going through the course, I will suggest you to take notes and revise it again and again. Otherwise, you will definitely confuse in many portions of the course. Thanks to Professor Andrew Ng.

The course offers indepth knowledge about neural networks from its basic building blocks to large deep learning models. Andrew Ng is definately one of the best teachers and his specialisation in Machine Learning is simply unparalled. The simple explainations and helpful tips throughout the assignments help a lot in establishing confidence while solving them. Highly recommend this course to anyone who wishes to understand the components of a neural network along with larger deep learning models.

This course is amazing. Andrew is an amazing teacher, you can see that he loves explaining this topic and understands it very well so he know how to put things simply. You may feel lost from time to time but the things that you may hardly comprehend are consistently reminded throughout the course. This gave me a great insight into the field of deep learning and I'm looking forward to learn more about it. I highly recommand this course to anyone who has basic coding knowledge and interest in AI.

The course is excellent. Andrew Ng is an expert on the field and explains everything in good detail. The course reminded me of my econometrics classes. It is always key to get your neck behind the mathematical part in ML/AI to fully understand the effects of your decisions as a data scientist. I love this course for giving me these details.

The Programming assignments where very useful to check your understanding. It took me some time here and there, but I went out with a better understanding.

This is my first deep learning course on coursera and I got in depth understanding about various concepts by taking up this course . As I am new to this domain, lectures gave me a clear insight and mathematical background behind deep learning. I enjoyed a lot in coding the concepts learned using Jupiter notebook and its like addiction and I cannot stop until I finish certain assignment exercise. Thanks a lot for this wonderful course and I hope to learn more courses of same caliber in future.

This was a very enjoyable course! It was very practically oriented, so everyone with some basic knowledge about machine learning, programming and neural networks could complete the course without too much of math background. I know this may seem as a disadvantage as well, but I think having good chance to do cool projects (because the programming assignments are cool) can motivate to further study of presented papers and textbooks well and eventually maybe use the concepts in research or work.

I checked some courses on other websites and the reviews were not that great. Most often, they don't cover the basics and only explain what Python functions do. This course is awesome. It covers the fundamentals of DL like a college class. This course is particularly appropriate if you have 0 knowledge of AI (and what to learn Python at the same time). Some sections are pretty basic (ex. calculus capsules), but you can skip them. There are 5 courses in the specialization. I highly recommend !

Great course to get hands on into implementation of neural network. It forces you to learn everything from scratch. Also I liked the notation used, and the clarity with which Andrew Ng explains the concepts.

Just one thing though, if the coding assignments had given much more work to us to figure out and do ourselves that would've been much more challenging. I felt that often the instructions given just before the exercise were pretty much a giveaway in terms of what code needs to be written.

Excellent intro course to deep learning. Andrew does a good job of taking students through the basics all the way to the development of a deep neural network. I particularly liked his depiction and explanation of the forward prop, back prop process through the graph. The assignments are challenging and superbly structured such that with some thought and effort you can succeed and actually implement the whole network. I would highly recommend this course to anyone curious about deep learning.

I haven't finished the course yet but I admit that Andrew is the best instructor I've met in my life. I've been taking a lot of courses online via different platforms; Coursera, Edx, Udacity and Udemy but this deep learning course with this very very simple explanations helps me a lot grasping main concepts in weights, bias, NN structure and more. I encourage everyone to take this course and learn from the assignments and pay a lot of attention to the material. It'd would definitely differ.

The course is pretty thorough with the theory as well as with the practicals. However, I did feel that I would have understood the implementation procedure much better if there was one more programming assignment per week. Regardless, I was able to follow the course and I'm excited to take the next course in the specialization. I feel like you would be able to follow this course much much easier if you have a good amount of experience with Python and at least a basic understanding of numpy.

After complete the Machine Learning Course, this one has been more easy to complete than it and, thanks to Python programming, easy to align with other related courses where there are programming assignments.

In addition, it's a pleasure to follow trainings delivered by Andre Ng. His teaching quality is outstanding.

The only "but" is that I missed to use DNN with multiple classification and not only binary classification. Probably it will be covered in next courses into the specialization.