Jun 26, 2018
Really, really good course. Especially the tips of avoiding possible bugs due to shapes. Also impressed by the heroes' stories. Genuinely inspired and thoughtfully educated by Professor Ng. Thank you!
Sep 13, 2018
This course is really great.The lectures are really easy to understand and grasp.The assignment instructions are really helpful and one does not need to know python before hand to complete the course.
创建者 Antoine C•
Jun 04, 2018
If you are already used to Python/numpy and you followed the free Machine Learning course from Ng, you really won't learn anything, apart from a new activation function.
创建者 Parth S•
Aug 10, 2018
Coding Exercise Were quite simple, a full length assignment would have been better.
创建者 Ashkan A e A•
Nov 13, 2018
创建者 Sundar S•
Nov 27, 2017
Fantastic introduction to deep NNs starting from the shallow case of logistic regression and generalizing across multiple layers. The material is very well structured and Dr. Ng is an amazing teacher.
创建者 Sergey G•
Jul 15, 2019
Dear Andrew! Thank you so very much for making me belive in myself as a machine learning engineer. Your lectures & excercises are like "shoulders of Giants" on which a good student can stand out high.
创建者 Juan P•
Feb 12, 2018
I would love some pointers to additional references for each video. Also, the instructor keeps saying that the math behind backprop is hard. What about an optional video with that? Otherwise, awesome!
Dec 02, 2017
Programming assignment is too simple
创建者 Mohammad G H•
Oct 01, 2018
Very basic level
创建者 Niloufar Y•
Jan 12, 2018
创建者 Antonio C D•
Jan 19, 2019
A good mix of theory and practice. The learning curve was perfect for me, and the course schedule is right if you study the material and work through the assignments in your spare time. Assignments are very well structured, I feel that trying to create the same implementations by myself (i.e. without the guides in the assignments and intermediate tests / check) would have taken 10x long.
创建者 Nikhil D K•
May 12, 2019
This is a good review of the concepts. It helped even more once I finished the course and reflected on the material by working out the equations for back propagation by my own hand. Looking forward to the next course in the series.
创建者 Jerry P•
Feb 03, 2019
Excellent course. Challenging, but doable. Andrew Ng is a great teacher. I learned about logistic regression, forward and backward propagation, code vectorization with numpy, activation functions, and many other topics.
创建者 Harsh T•
Jan 28, 2019
The course is good and it helps to clear the basic concepts of Neural Networks,
And the interactive assignments are just Awesome
创建者 Juan A O G•
Aug 30, 2018
TL;DR: It's a good course for people who are not familiar with neural nets. Otherwise, it feels kind of repetitive (I completed the course in 4 days)
Pros: Learn to implement efficient feedforward neural networks from scratch, by taking advantage of vectorized operations and caches; good understanding of how neural nets work and the reasons of their success; I loved how Dr. Andrew explained why we must initialize the weights to some small random numbers (I already knew neural nets before this course)
Cons: I expected to build neural nets in Tensorflow (after learning how to implement them from scratch); It'd have been good to include a gradient check (by computing the numerical gradient) to foolproof the backward pass; sometimes the explanations felt kind of repetitive (e.g. continuously going from one training example to the whole training batch). I would have just sticked to the batch learning after it was introduced
创建者 Jorge E C•
Oct 16, 2017
This course is good to just learn the terms and the basic aspects on architecture of deep learning. There is hardly any big explanations on the mathematical foundations of the topic which are of extreme importance to understand it.
It is a course for someone that dos not know much about neural networks or mathematics.
Is unfortunate that lead researcher in the area is able to say that it is not necesary to understand what a derivative is to be able to understand deep learning and the algorithm to update the weights of the network. I guess only for a first time course that is true, but I was expecting more from this course.
创建者 Miriam G•
May 18, 2018
Really just mathematical background knowledge. Nothing you would ever need, since there is keras. No own thinking during assignments neccessary, either.
创建者 Thomas M•
Jul 16, 2018
Course starts with a lot of math without any context what all those computations and parameters are used for or what they have to do with N
创建者 Loren Y•
Feb 06, 2019
The assignments are not good. Too easy and too much handholding. Also lots of technical issues.
创建者 Younes A•
Dec 07, 2017
Wouldn't recommend because of the very low quality of the assignments, but I don't regret taking them because the content is great. Seriously the quality of deeplearning.ai courses is the lowest I have ever seen! Glitches in videos, wrong assignments (both notebooks and MCQs), and no valuable discussions on the forums. Too bad Prof Ng couldn't get a competent team to curate his content for him. For such an basic level of content, you will find many other courses that are far better.
创建者 Andrew H•
Apr 28, 2019
Not enough explanation or support to complete the very vaguely worded assignments in anything like the specified timescales.
I respect the source of this course but as a teaching resource it is really very poor.
创建者 Ali A•
Aug 28, 2017
Terrible integration with Jupyter Python framework, end up losing 3 hours of work! Nobody responds from the courser team !
创建者 Kenneth T•
Jun 05, 2019
Great course, definitely taught me the basics of Neural Networks and Deep Learning as it's supposed to. Assignments are quite engaging when you try to thoroughly solve them. Even with minimal mathematics, the course will handhold you the whole way. Definitely a great course for anyone with minimal programming to get into. For me, the most challenging part was understanding how Python syntax worked with numpy. If you are taking this course I recommend taking your time with implementing the projects, they can definitely give you an understanding behind the logic of neural networks by following the code. The instructor is quite nice and warm, sometimes a bit dry, but nonetheless, he seems very warm; wanting to teach the next generation of individuals to do ML/AI. The course does have a few downsides such as how buggy the iPython notebook can be. This is the programming environment you will be using. An the video quality isn't always the best with the audio, but overall the content was presented in a great way and prepared in a manner in which you learn one step at a time.
创建者 William M•
Sep 04, 2017
I really enjoyed taking this course. I have taken one of Andrew's courses before, and they keep getting better. I have a background in development, and appreciated the use of python over octave. Andrew consistently strives to provide an intuitive feel for the topics he is presenting. The fact that he is able to provide a complex subject in a simple manner speaks to his mastery of the subject.
The course contained a great mix of theory and practical application of those theories. I'm looking forward to the next course.
创建者 john g•
Mar 28, 2020
What an amazing course. To be fair, I had completed Dr. Ng's course "Machine Learning" before taking this particular course, so some of the concepts, I was already familiar with. This course, delved deeper into the mathematics of Neural Networks and followed it up with coding assignments in Python. This course has provided a strong foundation for me to continue to build my knowledge base. To anyone interested in Deep Learning, take this course!!!
创建者 Malte B•
Apr 08, 2019
Great course to get a practical understanding of (Deep) Neural Networks. I would recommend to take Andrew Ngs "Machine Learning" course (also available on Coursera) beforehand, because the latter is much more rigorous when it comes to matrices operations. Thus it is unfortunately possible to just fill in the provided code in this course but don't really understand what it does.