AG
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.
OA
Sep 3, 2020
Great course. Easy to understand and with very synthetized information on the most relevant topics, even though some videos repeat information due to wrong edition, everything is still understandable.
By Joshua B
•Mar 26, 2018
Dr. Ng did a superb job going through the history of ConvNets. The examples, applications, and homework were wonderful, and I feel now much more skillfully able to read the scientific literature on the subject.
By Raj
•Nov 29, 2017
Perhaps the best course so far (course 5 is not out) in this specialization. I wish we got to train one of the CNNs and not have to use pretrained models but given the time and data needed, it's understandable.
By Gonzalo G A E
•Jun 15, 2020
Great course. One of the best of the specialization. Contains a thorough "theory" description of Convolutional Neural Networks as well as some pretty cool applications, and useful links to the original papers.
By Milton M
•Jun 5, 2020
A good compromise for someone new to computer vision. It covers almost everything you need to apply CNNs to real life applications, with a good balance between implementation details and theory under the hood.
By Sanket D
•May 29, 2020
Interesting SOTA ideas like CNNs, ResNets, object detection, etc are taught in a very intuitive mannar!
This course brings in an interest and lights a spark of the interesting things Deep Learning can perform!!
By Alok R S
•Aug 9, 2019
This course of deeplearning.ai covers almost every aspect of convolutional neural netwoks. There are proper hints in the programming assignments so that one can easily start with convolutional neural networks.
By Stephen S
•Oct 21, 2018
So far, this was the most useful and my favorite course of the series. Convolutional Neural Networks are really incredible study and have many useful features that you can implement to solve your own problems.
By Naveen D V
•Dec 6, 2017
This course has demystified the concepts of image recognition through a systematic breakdown of the main steps involved. It is very encouraging to learn algorithms that have been developed in the recent years.
By Tran H M ( H
•Aug 23, 2023
Andrew Ng has always been a person who can make concepts and phenomena from basic to abstract become very understandable. Andrew Ng's quote made the most impression on me "Don't worry if you don't understand"
By Neelkanth R
•Jul 17, 2022
I usually don't write reviews but I think it's helpful for the future learners to decide whether this course is worth investing money and time.
I would HIGHLY recommend this course, and i cannot stress enough!
By Govind S
•Jun 26, 2020
This series is a gem. Although, I have build some Deep Learning Models, this course provided me an in-depth knowledge of 'What, How and Why'. It enhanced my theoretical underpinnings of learning astoundingly.
By Juan-Pablo P
•Jun 1, 2020
A very good deep overview of 2D convolutions (and extensions to 1D and 3D) with real applications to object detection, face verification and recognition as well as neural style transfer. I fully recommend it,
By Sarwar A
•May 7, 2020
A great course taught by a great personality. It was very much exciting to implement some of the challenging as well as the exciting applications of deep learning like face recognition, object detection, etc.
By Aishwarya R
•Apr 23, 2019
Excellent course. Learnt not only about ConvNets but also about how to learn further after the course ends and apply the knowledge in practice. Thank you Dr. Andrew Ng and all other members of deeplearning.ai
By Akash S
•Apr 14, 2019
had a lot of fun in this course, i would, recommend every student to take up this course as it gave me an insight on how human eyes process images. this also helped me a lot to understand deep learning better
By Jonathan L
•Dec 18, 2018
Great lectures on Convolutional Neural Networks and their role in computer vision. The course could use more lectures on using tensorflow/keras as someone new to those modules may feel a little lost at first.
By Matthias P
•Apr 5, 2018
This was really fun. Great explanations from Andrew and engaging programming assignments.
A few hick-ups in the assignments here and there, but the community/moderators are extremly helpful!
Great experience!
By PLN R
•Mar 17, 2018
Amazing course, as far as course is concerned! Faced certain issues with the grader software; but it has nothing to do with the course content which is probably top notch! Looking forward to the final course!
By Carlo C
•Apr 15, 2020
Very very happy to attend this course! Andrew NG is always the best and explains always real well ! The exercises are useful and help to understand better the concepts. Very fascinating course! Thanks again!
By Christopher
•Apr 12, 2020
An excellent introduction to CNNs. Of the first 4 courses in the Deep Learning specialization, this was the most challenging, and had a number of great practical examples of CNN applications, including YOLO.
By Enrico C
•Apr 2, 2020
A great introduction to CNN, probably the best you can find on Coursera. I recommend using the message board where mentors will help to get to an in-depth understanding of what discussed during the lectures.
By Libing z
•Dec 18, 2017
It's a perfect course.
Andrew shows what CNN is, how it works and why it helps.
Andrew also show many case studies to make me understand the concept better.
The homework is really good to help me understand CNN
By Daniel G - O
•Mar 9, 2019
Very good content, but assignments have minor issues when sending for grading. These issues shouuld be pointed because sometimes the answer is correct but the grammar not and this is evaluated as a 0 grade.
By Amit P
•Dec 31, 2018
Very happy with the course - CNN was a concept I had heard a lot about and this course provided everything I needed to know to understand it in detail and implement it. Thanks Andrew for making this course.
By Roy M
•Jul 12, 2023
This class was very different from the first three. The first 3 covered the fundamentals. This one was much harder. It was not obvious where the concepts came from. Much closer to the frontier of research.