Apr 07, 2019
A bit easy (python wise) but maybe that's just a reflection of personal experience / practice. The contest is easy to digest (week to week) and the intuitions are well thought of in their explanation.
Aug 27, 2017
This is a very good course for people who want to get started with neural networks. Andrew did a great job explaining the math behind the scenes. Assignments are well-designed too. Highly recommended.
Apr 03, 2020
I give four stars because the course is great and the programming assignments too. But I think sometimes the programming assignments were a little condescending and easy. Don't get mi wrong, there were moments that I din't know what to do, but there were also a lot of times that all the procedure was explained.
Aug 25, 2019
This course was really clear my concepts of Deep Learning and how actually neural network works.
von Omar A•
Jul 22, 2019
If you have taken this course after ML by Andrew, you will see exactly the same material covered in 1 week expanded in 4 Weeks except using Python instead of octave or Matlab.
If you have calculus background I expect you to get tedious from elementary approaches in the lectures to get rid of Math and calculus.
Programming exercises in this course are very easy and below the level of first excellent experience with ML course.
There is no easy way to get lectures slides, No reading sections in this course. Like this course made to make systematic approaches to get things done without actual care about understanding the theories and concepts.
The good news comes when you have no previous knowledge about NN and elementary python skills, then this course is an excellent way for you to start.
Sep 09, 2017
The content is great and I learned a lot. Certainly there could be a lot more feedback by the instructor in the forum. My feeling is that the students are really left on their own. Good from one point of view (cause you really have no choice than crush your head on the problem for days until you understand or give up), bad from another (it takes a lot longer to clarify difficult points). Fortunately the forum is populated by very clever students that take the time to answer questions. As a beginner I learned the broad strokes and intuitions for NN in this course, but the details about certain formulas are still very obscure and I was hoping for a better explanation of those.
von Jérôme B•
Nov 16, 2017
To me, this is a failed attempt at simplifying those concepts. After spending hours trying to figure it out, now I find the algorithm behind the Neural Network very simple, and I can easily explain it to someone. But in this course I had to figure out by myself what was the point of those hundreds of lines of maths. So, very interesting concepts, but the "transmitting style" wasn't for me.
von Ofer B•
May 01, 2018
Very abstract, and the examples are not as concrete as they could be. I'd use better visuals to ensure that the concepts in each video are understood 100% visually.
von Aratz S•
Feb 27, 2018
Easy course if you have coursed the ML course before. I would like to see more explanations in detail. Still some bugs in the assignments... why???
von Muhammad A•
Aug 20, 2018
Great attempt but it failed to provide complete details. Specifically the project files and their loading mechanism
von Francis J•
Dec 29, 2017
too easy, suitable as an entry level class
von Tobias G•
Feb 21, 2018
Few Detail. Mathematics missing.
von David B•
Feb 17, 2020
This course is really quite bad. I'm not sure why the rating is so high. Probably because they are only prompting people who completed the course to rate it.
The main problem with the course is that It spends the majority of its time describing a byzantine set of notation while avoiding actually helping you understand how to apply the concepts you're learning. So you learn that a^[l](i) is the activation vector for layer "l" and example "i" but then you get to the python portion and, big surprise, none of that information is even slightly useful.
Even worse, the course hasn't chosen its audience. If you're good at math you'll be annoyed about the math explanations. If you're good at programming you'll be annoyed by the programming explanations. Rather than isolate that material in a way that lets people skip parts which they already understand, you get a really basic explanation of everything all globbed together.
Anyway, I'll still try to hack through this thing to finish it, I'm just letting you know that if you're underwhelmed, you're not alone.
von David W•
Oct 16, 2017
Great Presenter in Andrew Ng, on a topic of tremendous interest to very many.
However, unfortunately the grader seems to work only rarely in accepting submissions. Code that runs perfectly in the Notebook is repeatedly rejected by the Grader. Dozens of comments on these problems when the course opened two months ago. But still the problems have not been fixed!
And if you want to reset your Notebook for a fresh start , that may take hours or even days .
A pdf addressing exactly what one needs to do would be sensible. Instead one spends dozens of hours trawling round Forum discussions to guess what might actually work for the Grader. A most disappointing experience. Why is this considered in any way acceptable?
von Richard R•
Nov 18, 2019
Meh. I don't know why we are spending so much time in Week 2 talking about the math and how to not use FOR loops in week two when he STILL hasn't given any kind of overview about why we do this math, how we're going to use it to identify cats in pictures. Instead, we're just yakking on about math math math math math with NO context whatsoever. If I wanted a math class, I would have taken a deep-in-the-weeds math class. I expected a higher level of instruction for this higher level of abstraction but instead it seems that he just wants to talk about math and how to use vectors in NumPy. Zzzzzzzz.
von Domagoj K•
Aug 18, 2017
I am very disappointed with this new course concept where you have to pay 43$ a month to be able to solve a quiz. Coursera used to be famous for its free courses and now it just removes free features over the time. It has become another site with expensive courses. I watched first week lectures and this is probably my last time to enroll in Coursera course.
von Maxence A•
Oct 29, 2017
The programmation exercice are nice, but the courses are mainly about very basic linear algebra.
Apr 10, 2019
This course is really good but assignment given to solve is not understandable.
von Joseph K•
May 20, 2018
It will be a good course when you dump jupyter note books.
von Felix F•
Dec 20, 2017
giving low grade for ongoing delays of course 5
Oct 22, 2017
von Long H N•
Dec 10, 2017
von Amit P W•
Sep 30, 2018
Hello Andrew Ng Sir & Coursera Team,
Tell your instructors about yourself.
My name is Amit Wadhe. I am software engineer working in Walmart, Bangalore, India. I have 4 Years of working experience. Prior to Walmart I was working for Morgan Stanley. I have done my Bachelor of Technology in Computer Science and Engineering. I was always passionate about the computer from my school days. Out of curiosity I did my first C Language class in 10th Standard(School). That too with daily up-down of total 180km with train for one month from my hometown to Akola city. That time there was no computer courses offered in my hometown. After my schooling, I decided to go for engineering in Computer branch. I think that is enough in short about me.
Why did you take the course? How has it helped you?
I am working mainly on Java applications for last 4 years professionally. In last couple of years I realised that its not something which is exciting me, Its not something I wanted to work on. I was not sure what I wanted to work on, what excites me. I was hearing bits and pieces about Machine Learning and Artificial Intelligence since long from friends and colleagues. I was having perception about AI is that it's something big, something rocket science, something not for normal professional. But I got true trigger when I saw video about self driving car in silicon valley. That time I felt, Yes I wanted work on something like this, something which can be useful in real life, day to day life. I started searching about ML courses on google, I saw multiple courses on Udemy and Coursera. I red feedback about some courses. First place I started with some Udemy courses on ML for beginners but It comprised of only on how to code instead how it work internally. I was interested in knowing how something works internally instead of more in coding part. As I was Java developer I knew coding is not big deal. So I was curios about how ML models work internally, what is mathematics behind it, I was having interest in mathematics from the school days, though I did not score top. Then I started with ML by Andrew Ng on Coursera. After completing course, I felt like Yes, this is what I was looking for. Post completion my curiosity in deep learning has taken deep dive and I started looking for more courses by Andrew Ng on Deep Learning.
This course helped me to clear my understanding about how Neural network works mathematically. I was knowing bits and pieces about neural network steps like forward propagation and backward propagation but that was partial knowledge. After completing course I got that satiate feeling, Yes I know now, I understand it now in and all.
What did you love about the course? Tell them!
"I loved the bottom up approach of Andrew Ng Sir explaining concepts and Unveiling the treasure".
Irrespective of background I think anyone can understand the course with some knowledge on matrices and linear algebra. Recalling required knowledge learned in previous slides in short before diving into concept. Pace of course is also something which helps to grasp concept easily. Very intuitive examples helps to understand concepts faster. The example which I like most is about Neural network model of housing price prediction where Andrew Sir told intuition of hidden layers which is really connected to real life examples.
von Sarah R•
Dec 29, 2018
This course was insanely clear and meticulously constructed. As someone who does data science work professionally, I so appreciated the thought that went into the design of the videos and the programming assignments. You are seeing really exemplary code and also really sophisticated use of the Jupyter notebook! Also, the test cases are so well-constructed. You really get to *see* all of this stuff working or not with the carefully designed helper functions that allow you to visualize the decision boundaries and view training examples. Of course, the writing of these helper functions is no small feat. IT WILL NOT BE LIKE THIS WHEN YOU CAST OUT ON YOUR OWN. But, what this course does for folks (like me) who didn't have the benefit of a course like this in their formal schooling (perhaps they are too old and this stuff only got well-organized and codified more recently) is provide exemplars. Will your code always look like this for everything you build? No. But it shows you, using the exact technology that you are likely to employ professionally (tensorflow is coming up in the next course), what is possible. I look forward to rest of the specialization.
A note on the pacing: Perhaps because I am already very familiar with python, numpy, and Jupyter notebooks, I was able to complete this course in about two days (rather less than 4 weeks). However, I still got a ton out of it. I think it is paced the way it is so as to be viewed as more accessible by everyone, and also not with the assumption that you want to dedicate the majority of a weekend to it. Probably also there is something to the psychology of completing it so very ahead of schedule that the designers of this specialization are not altogether unaware of. But, if you, like me, know that you want a refresher on neural nets that is going to be practical and useful, in that it will help you both implement them AND understand what you're doing, this is a quick and effective way to jump back in.
Finally, since this is such a quick course, I really recommend NOT skipping it, even if you want to get to the more advanced topics in the rest of the specialization quickly. The course is so thoughtfully designed and concepts are introduced in a very specific and intentional way to make sure you understand each step before the course progresses. Based on having experienced this careful design, I expect the notational and programming conventions established in this course will make the next courses in the specialization more accessible.
In conclusion, this is I think the best online course with integrated programming exercises I've ever taken. I think it might be a standard-bearer for the whole field. Well done!
von Jeremy W G•
Apr 25, 2018
Copy&Paste from the survey I wrote earlier.
In 2012, I graduated with a statistics degree (BS) from the middle west where many companies hire data scientists to do simple analytics work. With my dream to do more predictive modeling work, I decided to go to the west coast and join the University of Washington to learn statistics in the master's program. One reason was that UW offered a great statistics program that most students chose to continue the Ph.D. program. The other reason was that Seattle had a few great high tech companies for me to explore opportunities at. However, although the MS program gave me a strong background in statistics theory, I found the industry moved so fast that my knowledge was falling behind the industry needs. In 2013-2014, I took Andrew's ML course on Youtube and Amazon hired me as a data scientist in the marketing department of Cloud Computing department (AWS). I figured that as a stats major I didn't have the knowledge in cloud computing or marketing, so in 2015 I took Coursera's big data specialization offered by UC San Diego, and the digital marketing specialization from UIUC. Later, I found another ML job at Amazon, using a lot of big data tools (Hadoop, spark, etc.) on AWS. After a year of settling down in San Francisco, this year, I decided to pick up the knowledge in deep learning. The first course of DL was fundamental but contained so much information that sometimes I needed to review several times because I forgot many statistical theories back in school. I thought it'd be very hard course but Andrew did a great job designing the curriculum where the theory and the application have a great balance for working people like me to start with. The amount of homework was much easier than I anticipated. I think for students who want to take the real challenge of coding, should hide Andrew's hint and write own functions. Overall, I like the Coursera courses and will continue to learn.
Jun 11, 2019
This course is great! I wish they would release a new version of the course where the math is visually explained instead of just handwriting by Dr. Ng. I think having to work with a small tablet really hapered his ability to develop the ideas as he was always trying to pack a lot of information on one ipad screen I would think that he could just stand in front of a white board and write on it with maybe hiring a sound technician this time? because despite the really high quality content of this course the audio is terrible and with the ipad screen not really doing justice to the writing, it really takes multiple viewings to figure out what's going on.
I would also suggest that Dr Ng really should explain when which one is which when he is using Y vs y and X vs x ... I'm sure it's crystal clear in his mind but for newbies like me, it can be confusing at times when there they write x but mean X (and vice versa)...
I still think this course is brilliant and it really cleared many concepts in my mind. It answered a lot of questions I've had after watching the fast.ai course. So if you're doing the fast.ai courses, you should definitely at least audit the deep learning.ai specialization courses and tbh, $50/mo is a steal for the calibre of information that is on offer (video/audio and ipad issues notwithstanding )
Work through it and you will find it extremely rewarding! Don't give up, keep going and if you feel frustrated, take a break and rewatch the videos the next day after a good night's sleep. It really helped me that I watched and rewatched video lectures, did the quiz, failed and came back to understand why I couldn't answer quiz answers. Good luck to all and Thank you to Dr Ng for making this available to us free of charge (if we wish to audit) I would buy the specialization though, since it is worth every penny and then some!