Jul 11, 2020
I really enjoyed this course, it would be awesome to see al least one training example using GPU (maybe in Google Colab since not everyone owns one) so we could train the deepest networks from scratch
Jan 12, 2019
Great course for kickoff into the world of CNN's. Gives a nice overview of existing architectures and certain applications of CNN's as well as giving some solid background in how they work internally.
创建者 AQUIB I•
May 24, 2020
Really an amazing course about CNN's. what an amazing instructor Andrew is. Totally recommended course those who want to learn CNN's from basic.
创建者 Ralph R•
Apr 27, 2019
I think it's a good idea to remove repeated parts in the videos. Also, put all pieces toguether to give a better overview of the object detection solution
创建者 Christopher H•
Feb 24, 2022
Customer service informed me that once a user completes a course, they're not permitted to access the assignments for reference again. This is a huge drawback to this platform, as that's where the real lessons are and essentially prevents a paying customer from being able to reference their own work. This is esspecially dissapointing given that I would have followed the instructions to download the Jupyter notebooks while in the class had I know about this bizzarre policy.
创建者 Basile B•
Apr 30, 2018
IoU validation problem is known but nothing as been done to resolv it
video editing problem
unreadable formula in python notebook for art generation (exemple :
What append ? that was great so far... =(
创建者 Shibhikkiran D•
Jul 8, 2019
First of all, I thank Professor Andrew Ng for offering this high quality "Deep Learning" specialization. This specialization helped me overall to gain a solid fundamentals and strong intuition about building blocks of Neural Networks. I'm looking forward to have a next level course on top of this track. Thanks again, Sir!
I strongly recommend this specialization for anyone who wish get their hands dirty and wants to understand what really happens under the hood of Neural networks with some curiosity.
Some of the key factors that differentiate this specialization from other specialization course:
1. Concepts are laid from ground up (i.e you to got to build models using basic numpy/pandas/python and then all the way up using tensorflow and keras etc)
2. Programming Assignments at end of each week on every course.
3. Reference to influential research papers on each topics and guidance provided to study those articles.
4. Motivation talks from few great leaders and scientist from Deep Learning field/community.
创建者 Zeyad O•
Apr 15, 2020
I'm Zeyad, an undergraduate of Computer Engineering at Alexandria University in Egypt.
Taking this course really helped me to learn and study this field and also to implement it. It helped me advance in my knowledge. This course helped me defining Deep Learning field, understanding how Deep Learning could potentially impact our business and industry to write a thought leadership piece regarding use cases and industry potential of Machine Learning.
This specialization helped me identifying which aspects of Deep Learning field seem most important and relevant to us, apparently they were all important to us. Walking away with a strong foundation in where Deep Learning is going, what it does, and how to prepare for it.
Deep Learning specialization helped me achieving a good learning and knowledge about that field.
Thank you so much for offering such wonderful piece of art.
创建者 Michael J•
Jan 2, 2019
A short (but cogent) overview of CNNs with a ton of references to read through and much more interesting assignments (than previous courses). I really enjoyed this course, I got a ton of exposure from it.
创建者 Devjyoti M•
Apr 22, 2019
This is one of the best courses for CNNs. This gives a very deep understanding of the concepts and helps to understand the brains behind the CNNs and their working in application based environments.
创建者 Daniel G•
Feb 13, 2018
Too much hand-holding during assignments, although still very good directions. Obviously the issue with the final programming assignment needs to be addressed. Fantastic lecture material, as always.
创建者 Tian Q•
Jan 1, 2019
Excellent introductory course for CNN. The basic ideas and key components are explained clearly. Coding assingments helped me understand the algorithm to every little detail.
创建者 Ambrish K•
Jan 28, 2022
Awesome course. Programming assignments give a real world scenario for trying things. Overall a complete course for studying and implementing convolutional neural networks.
创建者 Christian M•
Jun 11, 2022
Very good and clearly understandable videos!
I don't feel that I'm learning much in the programming assignments. I'm able to solve the assignments but still feel very insecure. I could never solve any of those tasks from scratch without the line by line guidance comments of the assignments.
But for a real life scenario there is no guidance...
创建者 Pascal G•
Jun 15, 2022
really good instructions - i also like that the original papers etc. were referenced for additional reading
personally, i would wish for the programming exercises to be with less 'pre-defined' code - especially in W4 in the neural transfer programming exercise, there was a lot of code written already in terms of preprocessing etc
创建者 Jose A O•
Jan 4, 2022
The programming assignments are great. However, there are too many constraints placed on the students. Many parts of the code are already provided, but in my opinion it would be more beneficial to allow the student to also complete many of these auxiliary codes.
创建者 Cosmin D•
Jan 4, 2019
Good content, videos have the occasional editing hiccups that also affect other courses in this specialisation. Assignments could be a little bit harder but do a reasonable job at familiarising with useful deep learning frameworks.
创建者 Sai B A•
Oct 9, 2019
The course content is great, I felt link the programming assignments should have more information on running the Tensorflow sessions and (optional )information for people who are not familiar with Tensorflow would be great.
创建者 Chris A•
Jun 10, 2018
Great course - only thing keeping me from giving 5 stars is the consistent problem with the notebooks/grader.
Mar 14, 2018
assignment of week 3 has a bug about calculation of iou
创建者 Moustapha M A•
Jan 29, 2018
I am a bit disappointed with this course , despite best efforts by Andrew. There is serious lack of rigor and while it is exciting to see things work , there is very little science to give us a methodical reason of why it works . In ConvNet we see the input data, a multi dimensional matrix get reduced in size using filtering and convolution operation techniques. From a mathematical point of view, this is clear and can be formalized but it is not clear why this process causes the ability to identify edges in a picture and evolve as we go deeper into the convNN to the real picture etc...
It seems to me this more like an alchemy rather then a rigorous scientific approach and this is why it was difficult to follow the exercises from the material of the course . I have to put concerted efforts to understand the literature which itself was not easy as it lacked rigorous mathematical and scientific approach ( why we have to increase the channels by multiples as we go deep into the conVNN ? etc...) . It seems to me the whole field is at its infancy with trials and errors - and more formalized approach is needed.
创建者 P R S•
Mar 30, 2022
The programming assigments do not teach me anything. They are as simple as uncommenting some code. Maybe creating optional/extra credit assigments to really test my understanding would have helped.
Not knowing things is not a big problem but not knowing that I do not know is. And these assignments help me do exactly the latter. The lectures have good academic content. However, they can be edited to remove mistakes/repititions with little effort.
创建者 Jacob K•
Aug 31, 2019
Great content, but this module gets far too buggy. The videos stutter and repeat as if they were going to be edited butt never were, and the programming exercises are so sloppy. The first exercise says, welcome to the second exercise, and congratulates you for finishing the course, even though the second assignment remains, that also says welcome to the second exercise! Loading a model hangs forever on one, and running the GAN crashes the kernel on the other. People in the forum have been complaining since at LEAST last year, and it's still buggy. This course content is great, but very shoddily put together compared to the rest. I am literally scared what week 5 will be like. Just clean it up guys. Hire an temp!
创建者 Younes A•
Dec 7, 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.
创建者 Bilal A B•
Aug 19, 2019
I know I am giving 2 stars :( but unfortunately this course was bit difficult and I don't know why Professor didn't first gave few fundamental concepts of computer vision. It's just my opinion maybe I'm wrong, maybe I'm right. But honestly we should have gone through some basic C.V. so that few students like myself can get a better understanding rather than directly diving into use of DL in CV.
创建者 Milica M•
May 10, 2020
boring and uninformative; could use improvement and some rehearsal before giving a lecture; boring and unorganized delivery; slides are horribly unorganized and boring; often times very confusing and hard to follow; should minimize the number of times the instructor references basic math and should use that time to motivate the concepts and applications
创建者 Ankit A•
Jul 10, 2022
its very important that few projects be given with raw dataset and we should be able to build projects right from scratch. That gives more confidence, else this course is only limited to hugh school students and not at all for sometime who wants to make a career into this - projects are 90% of the learning and not theory.