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Learner Reviews & Feedback for Convolutional Neural Networks by DeepLearning.AI

4.9
stars
42,029 ratings

About the Course

In the fourth course of the Deep Learning Specialization, you will understand how computer vision has evolved and become familiar with its exciting applications such as autonomous driving, face recognition, reading radiology images, and more. By the end, you will be able to build a convolutional neural network, including recent variations such as residual networks; apply convolutional networks to visual detection and recognition tasks; and use neural style transfer to generate art and apply these algorithms to a variety of image, video, and other 2D or 3D data. The Deep Learning Specialization is our foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology. It provides a pathway for you to gain the knowledge and skills to apply machine learning to your work, level up your technical career, and take the definitive step in the world of AI....

Top reviews

AV

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

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.

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4451 - 4475 of 5,570 Reviews for Convolutional Neural Networks

By Emad o H

•

Sep 5, 2021

Everything was absolutly great and undoutdetly quite usefull , I have one complaint though , if I had a problem in assignments( mostly related to grader output like U-net segmenation which I passed all the tests successfully but somehow my grade turned out 66) and I 've put my problem in discussion no mentor would answer it and if they did it was always the same "The topic has been moved somewhere that I don't know"

anywho it was great thanks for allowing me to take this course , it really helped me I hope I could return your favor

By Mikheil A

•

Jun 9, 2018

Very good course. I only wish there were even more examples with harder homework. I did every homework in less than an hour and felt like I still couldn't reproduce much on my own after taking a class. The lectures are great and cover most of the material you need. But as programming assignments go, it is still a very introductory class and you are definitely not ready to write much on your own afterwards. But I think they can improve a lot with a few more homework jupyter notebooks that are more advanced that their current ones.

By Joachim H

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May 6, 2018

Course provides good overview over state-of-the-art techniques in computer vision. The lectures are mostly clear although week 3 and 4 do lack some explanations on how these systems are trained. E.g. the style transfer lecture should emphasize that optimizer acts on the pixels of the generated input image using the without altering the weights of the network. In terms of the programming exercises, I would prefer to work through the code to better understand the structure, rather than just filling in bits. Still a great course!

By Ben E

•

Jun 27, 2020

Great explanation of advanced topics in deep learning and computer vision. This course deepened my understanding of convolutional neural networks in significant ways. The videos could use a bit more editing to remove repeated phrases, but it didn't distract too much from the learning. The projects are very good at giving hands on experience with the concepts and the tools. It would be great if they could be updated for newer versions of Kera and Tensorflow. Overall, I would recommend this course to anyone interested in CNNs.

By Charles S

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Jan 4, 2018

Excellent lectures with really engaging explanations and examples. The programming exercises are also really well structured and require real work and understanding where code is required. It is very exciting when you get the models to run and produce a result. One concern: My sense is that we are moving a little fast on the overall process of solving a problem. That is, the programming exercises are so well structured that I am not confident that I could solve the problem without the exercise frameworks.

By RAJEEV B

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Nov 14, 2017

The course content in the video lectures is very good -- all the visual explanations and Andrew Ng explanation is easily understandable. But as far as assignments are concerned, many of the functions are readily implemented and just called for use. It would have been good if the student is guided in implementation from scratch. Though a top view understanding from theory to implementation is obtained with solving the assignments, it would have been more profound if everything could be implemented from scratch.

By Matías B

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May 18, 2020

The material in the course is very good, specially the notebook exercises.

There are some technical aspects that prevent me from giving it 5 stars.

Namely, the english captions of the videos have too many mistakes, considering how easily they can be fixed.

I don't see the point of having an extra 'Reading'material with corrections for the videos. Why not adjust the video once and for all?

Finally, the number of clippings and strange cuts and jumps in the videos have increased with respect to previous courses.

By Ernest W

•

Jul 4, 2021

Comprehensive course with a huge dose of knowledge about different CNN architectures, image recognition and neural style transfer as the last assignment. The lecturer teaches a lot about the theoretical part and programming assignments are demanding. However, after completing the course, I don't feel confident enough in using Tensorflow as there are some exercises that I've finished mostly by trial and error without actually understanding why they worked with so many other questions about theory in my mind.

By Mark S

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Oct 9, 2019

Overall a very good course. Assignments have errors in the code. This is documented in the discussion centre going back a couple of years, mentors help explain, but mentors cannot edit to fix the code, and the course supervisors have long since disappeared. So you have to submit incorrect code to pass, then fix the code for your personal private code store - as the fixed code generates the correct numerical answers that unfortunately do not match the numerical answers that the grader requires to pass you!

By Nicholas K

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May 25, 2018

Excellent survey of the area. However, programming exercises vary frustratingly between cut-and-paste and obscure tricks that require burrowing through the forum. Long-term value significantly reduced by the apparently intentional decision to not support efficient download of needed material. Unfortunately, Coursera's notebooks are also not stable, routinely resulting in lost work, followed by multiple tries to log back in. Love the content, but the rest of it seriously reduces productivity of study time.

By Noam S

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Nov 28, 2019

The course material is very interesting, but also somewhat hard. It takes everything we learned in the previous 2 courses to the next level.

This is a good thing! However, the programming exercises do not really require the student to understand much. In most of the exercises I copy pasted from the examples and used some trial and error. Contrary to the previous courses, I feel that the exercises were something I did to pass the course - and they didn't really help me understand the material better.

By shiv c

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May 22, 2020

Overall, this course clearly explained the basics of CNNs. However, the neural style transfer network could use more details. For example, after calculating the loss function which layers are updated in the network? Is it all of them or only the ones used to find the style image. Also, 'a_G' not being evaluated in the assignment wasn't clearly explained. Lastly, please have a couple of more assignments on tensorflow. I've done the earlier courses but they don't give enough of an understanding.

By Gerald B

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Apr 1, 2018

As usual, very informative and challenging at times. Andrew does a great job of introducing complex topics. I find that some of the quiz questions are ambiguous and result in the reader selecting the wrong answer. The hands-on assignments are generally of the right level, although a longer introduction to Tensor Flow would be useful. The delayed execution nature of TF can be confusing at times and it is not always clear whether the formula should be using TF functions or Numpy functions.

By Jon K

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Jan 23, 2021

Significantly more difficult than the other courses in this specialization, but a good course none the less. Lecture quality is excellent, and the programming exercises are good. The reason why I'm giving it 4 stars instead of 5 is because they really need to spend at least 1 week in the course going over TensorFlow by itself. It is not an intuitive architecture and is almost like it's own language. I think students would get more out of this if they were better versed in TensorFlow.

By Sudhir K

•

Nov 24, 2019

I think the programming assignments were really good in hand holding and making students learn. As part of the course, I wish there is a open ended project to achieve an accuracy of X% on a given dataset. This could help challenge students to have some comprehensive(whole model building) experience. I understand that this increases the course completion time as a trade off. But I think an open ended project with a predefined dataset would have added well rounded value to students.

By Indira I

•

Nov 20, 2017

Awesome course - a lot of material and complexity covered with good examples and assignments to understand the range of architectures of CNNs and their applications. Really enjoyed learning.. But working on programming assignments was frustrating given the grader shenanigans.. Coursera service on this is not great and sometimes tips from Mentors didn't really help. Greater attention must be paid to ensure that learners have a consistent experience with programming assignments.

By Joe M

•

Oct 6, 2019

Another great course in the series. The later labs were difficult, some additional time in the videos on TensorFlow concepts would be helpful, hit some frustrating points in the weeks 3 and 4 labs. Also helps to have background with linear algebra (or it's a tough intro and notice to study up on the stuff!) Overall another awesome survey of the state of the art, lots of practical advice along the way, the links and discussions to the underlying papers were great.

By Keyan P

•

Dec 3, 2019

One of the most clear convolution explanations ever! Loved the mostly recent algos discussed, too bad all important papers don't have breakdowns like that!

Negative:

-Video editing has gotten worse in later courses, lots of areas where Andrew clearly thought he would be edited so he repeated himself

-Quiz feedback is non-existent. There should be blurbs explaining why answers are right and wrong, instead of just saying it is wrong or right with no supplemental text

By Habiboulaye A B

•

Nov 19, 2017

Nice lectures and exercises.

Unfortunately, although there are some problems with some expected results:

1/ Face Recognition: The Grading Process has some bugs, issues if TipleLoss function

2/ StyleTransfer: model_nn fonction give wrong "expected value" by using indication to define cost. It seems like the problem come from tf.square that not gives the same results as np.square (correct value)

Please fix these issues, then then lecture will be perfect.

Thank you

By Francois L

•

Jul 21, 2018

Prof. Ng is a very good teacher and the course is content-rich and well organized, but there are two things that could be improved. First, there are many hesitations and reformulations that could be removed from the videos. Second, and most importantly, the assignments are a bit too easy. The answers are almost given in the questions, and if you know how to translate equations in Python you can manage to pass without really understanding what you're doing.

By Nicholas P

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Oct 8, 2020

Excellent course, but some of the most technically difficult material I've ever encountered. Gave me a solid understanding of the theory behind CNNs and their applications. However, it doesn't go deep into how to program using tensorflow and keras and holds your hand through most of the assignments. Overall I think you'd have to supplement this course with some keras tutorials and lots of practice to be able to implement any of the assignments in the wild

By Okundu O

•

Dec 12, 2017

I think I learnt the most from this course compared to the other deep learning courses. The material was well presented and the labs were also very hands-on and had enough to help understand how to implement real world problems. There was an issue with the last assignment where the code which matched the output failed the grader (triple_loss) which I think should be looked into.

Many thanks Andrew Ng and his team for another very well organised course.

By DANTE K

•

Feb 26, 2021

Great intro to CNNs and their applications. If you're interested in anything regarding image processing or computer vision, this is a must!

Only negative I have to give is the same that applies to the other courses, which is the TensorFlow part is taught without much explanation and you either copy/memorize the code or you bash your head trying to understand how it works. This course also works on Keras though which I felt was taught so much better!

By Péter D

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Nov 4, 2017

excellent course, great lectures and interesting excercises - the only dowside is the non-existent difficulty of the quizes / prog assignments, you should pump it up a bit (if the students are forced to think through the algorithms they better understand them, in the current state everything is prepared, you just have to fill in some tiny details - you don't even have to understand what you are doing to net a full score, which is really sad)

By Patrick F

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Jul 5, 2020

The course is really of amazing quality. The only reason I am giving only 4/5 is because there several slide pack not available for some video, and coursera/instructor could have made it easier for the student to follow along with a proper slide pack and taking note. Instead we have download 2-3 slides per video, concatenante them all, or even take screenshots when slides are note provided. Appart from that bémol, that course is gold.