Great introduction to specialization and I'm sure rest of the courses will go further teaching me about AI. Author explains the basics of neural networks in understandable and enjoyable way. Programming assignment are sometimes too simple but at least I can understand what I do step by step even if I dislike Jupyter as development environment. Sometimes the audio quality isn't good while listening on headphones.

The course couldn't have been any better, The only thing that confused me is the lecture in which Andrew was trying to clarify why using a shallow NN with one hidden layer instead of an L-layer NN will result in accumulating 2^(n-1) units. I had to replay the video and think about it for some time. I consider this the only thing that requires attention and deeper clarification. I really enjoyed this...

Thank you!

This course is a must-invested course with respect to time and efforts who are serious to kick-start the career in A.I/M.L. The quizzes and assignments are so well-designed that one can check his/her progress after each week. The practical insight which one gains will include: back-propagation intuition, feed forward propagation,gradient descent algorithm, logistic regression and finally the Deep neural network.

This is a great course that introduce you to that fancy and complex looking term (neural networks). The instructor introduced the topic by taking small steps , which is the best part of the course. The teaching in videos is really up-to sufficient depth to grasp the concepts and the programming assignment were really well organised and gave to actually implement and test the theory stuff,really liked the course.

Truly a very insightful introductory course to Neural Networks. I am no longer allegic to mathematical notations, linear algebra and vectorizations. Following the explanations in this course is a fast track to understand the basics of Neural Networks, much faster than reading online references or textbooks. After taking this course, I am very much looking forward to take other courses, especially from Andrew Ng.

THIS WAS A VERY INTERESTING AND USEFULL INFOS AND THE MOST SIMPLIED CODES IN PYTHON THAT I EVER SEEN . THANK YOU VERY MUCH PROF ANDREW AND HIS TEAM,

BUT YOU NEED TO KEEP SHORT THE ASSIGNEMENTS PLS . THESE ASSIGNEMENT TAKES A LOT OF ENERGIES , TIMES , AND DAYS TO COMPLETE AND TO DO THE REASEARCH ALL OVER THE INTERNET IN ORDER TO COMPLETE THEM , PLS NEXT COURSE , TRY TO SHORTEN THESE ASSIGNEMENTS PLS PROF.

THANK .

This is probably the best Coursera course I've ever taken, and I have already taken quite a few in the past. I loved it, the discussion forums were useful, every single explanation made its point and was clear enough, every content and assignment was insightful, the coding tasks made sense and were well guided and explained... So glad I've taken this course to start off on this field, totally worthy! Thank you.

goes in depth with the intuition behind neural networks, which makes the programming so much easier (as someone who has struggled with that). i did another course before this for NN and it was nowhere near as good. also teaches good programming practices (assertion for instance) and ensures we know the importance of matrix dimensions. saves so much headache. highly highly recommend this course worth every penny

Very nice, concise course! The programming workbooks must have been a huge amount of work. As a result the assignments were extremely easy (copy & paste mostly), but if one tries this "out in the wild", this requires quite a jump in programming expertise. For this reason, I would have preferred some more "open" assignments where the student has to navigate the python pitfalls himself. Probably not realistic ...

Excellent course for beginners . I had no idea about deep learning when i enrolled in this course but now i have some clear intuitions regarding neural networks . However in this course the professor only explains about the process and he will not teach about implementation in python . It will be great if he explained about implementation part also . Overall it is the BEST course for beginners .Enjoy learning!

Really easy to understand, though you'll have to work a bit extra to get clear about matrix dimensions.

Also, exercises were spoon feeding, i.e., fill in the blanks type. So if you have to learn from them try implementing it on your own personal Jupyter notebook from scratch, without looking at that exercise.

Overall, you'll need to do some personal practice apart from the course exercises to have a better grip.

This is a well-designed course even for beginners but it requires some previous python and math (algebra and calculus) knowledge. All the materials are well presented and the assignments gradually help one understand how to connect the dots in deep learning. For those without computer science, engineering, or math background like me be ready for 4 intense weeks with lots of learning, trial, and error. Have fun!

The course is structured very well for a beginner like me to understand the neural network concepts, particularly forward and backward propagation process and mathematical relations are dealt so nicely and easy to understand. The practice sessions and programming assignments have given me a good insight on implementation steps/functions involved to build quintessential neural network model of Deep learning.

Really loved the course, I would highly recommend taking this course to anyone interested in learning Deep Learning with good foundations and start applying it in projects. There are other courses that I kinda skimmed around but this course has the best balance between application and concepts. It is to be noted that I had already played around ML before so I wasn't so eager to get into the application so fast.

I consider this course is essential among the five courses in Deep Learning Specialization. You are recommended to spend enough time to dwell on the programming assignments in this first course. Don't be in rush to complete this course. Do understand the programming assignments line by line. This understanding will help you roughly understand the internal process of Tensorflow or other deep learning frameworks.

Speedran this in 1 week. 11/10 now I can classify Andrew Ng vs non-Andrew Ng images.

Really though, Andrew Ng did a good job explaining most of the concepts. There's quite a bit of math, but even without a strong foundation in mathematics, you should be fine because Andrew Ng has his way to help you understand the code regardless. Can't wait to finish more of his courses so I can classify him in even more ways.

This is an exceptional course, I thought deep learning would be so hard to follow but with Andrew Ng sir's explanation and the programming assignments made it very easy to follow and understand. I recommend this course to everyone who can be a beginner or an expert, this will change your views upon deep learning. Andrew Ng sir you are my hero, I really like his teaching method and how he structures the course.

This course provides outstanding introduction into the subject of deep learning. Andrew Ng step by step explains from very basics of the subject (logistic regression) to very complex deep networks consisting of several hidden layers. Course also provides with amazing programming exercises which builds a clear understanding of what you are doing and how you apply your theoretical knowledge. Thank you very much!

I feel the course pragmatical since it is not too theoretical. I learned useful advice for consistent coding to prevent bugs thanks to hand-holding instruction by Dr. Ng.

Also, I like the interview videos of the heroes to peek at the cutting-edge of dynamic research in artificial intelligence.

Actually, I pursued similar program in Japan but it is a quite good to reorganize and brush up my knowledge and skills.

Thank you so much, Professor Andrew Ng. This course showed me how big and complicated deep learning is but the way you teach I understood almost everything, and for things I did not I will go through them once again and I am sure I will understand them. I now have a deep understanding about Neural Networks and I am going to dive in deeper by completing the specialization course of the namesake "Deep Learning"

Very good course. If you've taken the machine learning Andrew Ng course, there is a lot of overlap, to the point of redundancy here. It can be good to reinforce concepts, though, and is required for deep learning specialty. If new to this course, it's very good. The exercises are well done in Jupyter notebooks. My only complaint is with regard to technical difficulties saving notebooks for project submission.

This is an excellent course that explains the basics of neural networks. Other sources present a vague idea behind the algorithms and often fail at explaining the essence, but this course goes straight to the foundation of neural networks with a combination of linear algebra and calculus - so eventually nn become a simple set of equations which are easy to comprehend. By far the best course I have ever taken.

Presents the crux of deep learning simply and understandably. I wish Prof. Ng would go a bit more in depth into the derivation of the backward pass equations, but the course was otherwise good. Programming assignments are a mechanical at times because they spoon-feed you the answers. I found it amusing that they assume calculus is beyond the target audience but regularly assume some basics of linear algebra.

This course is great for beginners in deep learning field. It helps in understanding the main concepts such as: how to design model, the main parameters and the hyper-parameters, how to test the model and the difference between the N layer neural network and the deep neural network. The instructor is perfect and the whole course is amazing. I will recommend it to anyone wants to begin in deep learning field.

First course of the Deep Learning specialization. I did it after the Machine Learning course. It is a well structured and progressive course that really explain what is a deep neural network and we build the algorithm in Python step by step. The programming assignments are well prepared and finally give you a good implementation of a neural network as well as a good understanding of how the whole thing work.