one of the excellent courses in deep learning. As stated its advanced and enjoyed a lot in solving the assignments. looking forward for more such courses especially in Natural language processing
The hardest, yet most satisfying course I've ever taken in deep learning, by the end of the course I was doing stuff that was borderline sci-fi and that was just "introduction" to deep learning
By Mauro D S•
Good intro to deep learning (RNN's well explained! Good job.)
By Saraansh T•
A job well done. Quite responsive community and support.
By Seongeun S•
Great course to get a first view of deep learning !
By SABYASACHI B•
some lectures can be given at a slower pace
By Eric V•
Great course with challenging assignments.
By Ting Y•
Comprehensive intro of deep learning
By Siddharth P•
Tensorflow 2 would have been great
By Olayinka E O•
Lots of Interesting exercises.
By Tadas Š•
Quite good - not too basic.
By Abhishek S•
Challenging and helpful !
By Chi E•
I love the material!!
By Heider D L•
Really hard lol
By Vinícius L•
By Robert K•
I've dived into this course only AFTER completing Andrew Ng's specialization "deep learning". In that sense this was a nice "revision" with additional set of exercises. Some of the topics introduced were nice exercise in ultimately "testing" your knowledge from other sources. Having said this, you really need previous exposure to machine learning, and I'd also say - deep learning.
But it doesn't give much beyond this point. Lecturers vary in terms of knowledge, or rather the ability to clearly present it. Coursera serves might not be enough for most exercises, and it pushes you to set-up your own machine (if you have a proper one) or configure one on the cloud. With many services it is rather easy now.
Overall, I recommend it as a review, an introduction AFTER some exposure. Some additional material might be new to you, but no necessarily if you followed other courses. I am more eager to look into further courses in the specialization.
By Angela W•
The topics are great, but many of the speakers have heavy accents and the written transcript is often nonsensical. All in all, I did not enjoy watching the lecture videos and found the course pretty tedious. What is very cool is that the programming assignments can be done on google colab (with instructions) where you can use GPUs for free.
In summary, I feel that I did learn a lot and the idea of the course is great, but faced with the prospect of having to watch more of these videos to complete the specialization, I cancelled my subscription after finishing this course instead.
By Ramin A•
Overall I enjoyed the course, but it lacks structure. Some materials are assumed to be well known by the learner and surprisingly some easier ones are not. I like to see the math, but it needs more materials to support it. Most instructor's have very heavy accent and tend to speak too quickly, I find myself rewinding multiple times just to figure out what was being said. Homework's are not too difficult, and are enjoyable. Except for the last one where you need to wait for a peer review. I think this can be a flagship course with more efforts.
By Hermon A•
The explanation of TensorFlow is not enough and the programming homeworks have already a lot of already written (because, i would be very difficult to programming the all of the homework by ourselves in this stage of learning). I think it is better programming homeworks with examples more easy, but with more programming by ourselves.
At least, I think it is already well enough for the final evaluation, the automatic correction and then, the correction by peer only delay the evaluation.
By RJ C•
I could not understand what the lecturer in the second week was saying. Overall good content but awful presentation. Exercises are ridiculous, my code is working fine, but since I do not use the same function as teachers and I do not get the same result to 0.00001, I cannot pass the class. Definitely will not be renewing this class. Think twice before signing up..I am sure the guys that made the class are really smart, and the content is high quality, but overall I am disappointed.
By Juan C E•
The quality of some of the video session is not good, especially for RNN's. Very general, badly explained and little practical information for the practical assignments. Yor have to "learn" the material, not just look for additional information, from other sources.
The pratical assignments are note always well designed, and some are full of flaws. After many many hours of dealing with some of them, you get the impression that you've passed the assignment but not learned much.
By Carlos V•
The Course is good, probably should be called introduction to advance deep learning, the complexity of the assignments make you put lots of efforts around them, that is rewarding at the end, make sure you have plenty of time to dedicate to this Course, one thing the Course could improve on is to try to minimize the switch between libraries and the low-level coding with high-level coding between TF and Keras sometimes it creates confusion.
By Zhaoqing X•
Well, I think it's a good course for introducing us to Deep Learning and it has better(tougher) assignments than Andrew's. It also covers more knowledge than Andrew's. But the quality of the course is not that good. The Russian accent is not important because my native language is not English as well, but the assignments are frustrating. The mentors cannot answer the questions that widely appears in the course.
By Raffael S•
Sadly, this course leaves out a rigorous introduction to all the math behind Deep Learning. Also, it would have been nice to give an assignment to implement a conv net from scratch even if it is just a small one. Additionally the coding tasks felt like you only had to fill in coding related problems, like splitting text and setting up one-hot encodings. All the neural network fun was already prefilled.
By Dmitry Z•
I'd prefer a way more detailed explanation of different architectures and alghoritms + a more detailed explanation of Keras and TF. Programming assignments are more like quizzes where you don't write the code yourself, but rather fill in gaps, because during the course you don't study how to create a fully functional system from scratch.
By Jae L•
Lecture slides need more written explanations, information, and math. Also, jupyter notebooks seriously lack information describing codes, explanation in neural network functionalities, and architectures. Please, practice clearer speech speaking, if it's hard to change, supply detail written notes to read for students.