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Convolutional Neural Networks に戻る による Convolutional Neural Networks の受講者のレビューおよびフィードバック



This course will teach you how to build convolutional neural networks and apply it to image data. Thanks to deep learning, computer vision is working far better than just two years ago, and this is enabling numerous exciting applications ranging from safe autonomous driving, to accurate face recognition, to automatic reading of radiology images. You will: - Understand how to build a convolutional neural network, including recent variations such as residual networks. - Know how to apply convolutional networks to visual detection and recognition tasks. - Know to use neural style transfer to generate art. - Be able to apply these algorithms to a variety of image, video, and other 2D or 3D data. This is the fourth course of the Deep Learning Specialization....



Sep 02, 2019

This is very intensive and wonderful course on CNN. No other course in the MOOC world can be compared to this course's capability of simplifying complex concepts and visualizing them to get intuition.


Dec 12, 2019

Great Course Overall\n\nOne thing is that some videos are not edited properly so Andrew repeats the same thing, again and again, other than that great and simple explanation of such complicated tasks.


Convolutional Neural Networks: 51 - 75 / 4,203 レビュー

by José D

Oct 26, 2019

This is course 4 of the Deep Learning Specialization. Things get harder in this stage as we go through Convolutional Neural Networks (CNN), that are more difficult to understand than "simple" neural networks (Course 1 now looks easy to me...). Well-designed programming assignments along with nice course materials. You will understand how work image recognition in general, which is used for many problems like: image classifiers, face verification/recognition, object detection in real-time (YOLO algorithm) and even artistic creation (Neural Type Transfer). An important course that is worth the time and effort. Iv' learned many things.

by Glenn B

May 31, 2018

Great topics and discussion, however the lectures started to gloss over the details of implementation which were left entirely to the exercises.

Use of Tensorflow and Keras required more background to clearly do the exercises than provided in the tutorials or examples.

I get the dynamic aspect of writing the lecture notes in the videos, however the lecture notes should be "cleaned up" in the downloadable files (i.e., typos corrected and typed up). Additionally, the notes written in the video could be written and organized more clearly (e.g., uniform directional flow across the page/screen rather than randomly fit wherever on the page.

by Charles M

Aug 20, 2018

Excellent material taught by the best, Andrew Ng. Very relevant to my interest and career goals. The object detector section was especially helpful for my work at a small startup. The material is top notch and more detailed of what I got during my masters in computer science. The code examples and assignments are very fun and rewarding. There are some slight glitches during saving and submitting assignments, so i always made a backup copy. Other than that, the course was great. I skipped directly to convolutional neural networks since I am already familiar with deep learning. However, i eventually wanted to finish all 4 courses.

by Anna V

Apr 30, 2018

Great diving into the cutting edge computer vision algorithms (such as YOLO), the state of the art CNN architectures(ResNet, VGG, Inception Network, Siamese Network), with a variety of applications of this architectures and algorithms, such as self-driving system, neural style transfer generator and face recognition and verification! Very simple and understandable submission of very hard to read and realize machine learning papers, perfect explanationof the cutting edge machine learning algorithms, architectures and approaches used in this field. I'm so pleased with the quality in this course! It helped me VERY MUCH! Thank you

by Artem M

May 19, 2018

This course is not very deep mathematically (which is not very good. Again, additional material on the derivation of gradient descent for filters could be provided) but it is deep learning, so it is expected. On the other hand, the contents are just wonderful. It was my first exposure to computer vision/CNNs, and I can say that the introduction here is absolutely the best. It covers a lot of topics (new and not so new). Finishing this course will make you well aware of how convolutional NNs work and point you towards particular areas depending on your interests. By far the best introductory course in this specialisation.

by Noor A

Mar 28, 2020

Great introduction to the topic. For people who would like a case study oriented course this is it. The amount of content is also very impressive even if slightly dated. I have spent almost 2 years actively doing research and working with CNN's but the course still had a lot to offer in terms of content. It would've been the perfect starter pack if there was a section on image segmentation. Maybe there could be a complete course on Ng just covering case studies and research papers. Regardless attending this course is a must. The assignments are well curated and I can image will be extremely forgiving towards beginners.

by Guy M

Sep 05, 2018

This is a great introduction to what CNNs are and how to implement them in a framework. My one almost-gripe is that when it comes to the assignment it can leave you floundering because there is minimal coverage of some of the requisite knowledge of running a NN using the framework. I'm all for making students work things out, but in one or two ways it was just a bit too high of a step to expect a student to climb. I'm talking here about the steps required to actually run a NN and then make a prediction. By contrast, several of the much easier steps might have a hint such as "You might find the ... function useful".

by Zhiming C

May 29, 2020

This is a very good course. It contains quite a lot important CNN topics and models, which are state of art and very popular nowadays in industry. Although the contents are only aiming at some introductions of these topics, we can still get a very good impression of what it is and how it works. The exercises are relative simple, because to implement a real network and to train it will take quite a lot time. I think if there would be a implementation of e.g. model in detail, we can be more familiar with the contents. All in one, it is a very good course and covers a lot of useful models and information!

by David R R

Nov 28, 2017

This is a very interesting and functional course. Week 1 gives you the basic ideas behind CNN and it is very easy to follow the videos. The next weeks gives you what are under the hood in object detection systems, other CNN architectures, style use... I recommend this course

Este es un curso interesante y sobre todo funcional. La primera semana te enseña las ideas básicas detras de un CNN ademas de que son lecturas faciles de seguir. Las siguientes semanas te enseñan los "secretos" de los sitemas de detección de objetos, otras arquitecturas de CNN, uso artistico de las mismas... Recomiendo el curso

by Sourab M

Dec 03, 2018

One of the best courses for learning deep learning concepts for computer vision. It provides a deep understanding of convolutional neural networks and the various architectures used by state-of-the-art models. We get to learn various concepts of computer vision - image classification, localization, image detection, face verification, face recognition and neural style transfer. Ii would have been better if course also covered image segmentation. We get much needed hands-on through interesting assignments and along the way we get to learn Tensorflow and Keras. Thank you for this great course :)

by Ayush T

Mar 02, 2018

Like the other courses of this series, this course is really good. In this tutorial I have not only understood how to implement things but I have also learnt what's the math behind those things. It is important at-least for me because it allows me to do more experiments with CNN's or in general Neural Networks. The thing which I like most about this course is its programming exercises.

I recommend this whole series to those people who want to learn some advance machine learning stuff like GAN, variational autoencoders and Reinforcement learning. This series will help as a strong foundation.

by Yilun Y

Apr 06, 2019

Overall an awesome course, however, it somewhat lacks some important topics and models such as SSD, Faster RCNN, mask RCNN, etc which are even more frequently mentioned in literature and applied in real world projects. This course really sparked my curiosity and passion in deep learning, I actually learned the models mentioned before by reading the original paper and many useful blogs. This is a long but rewarding journey, I would also like to see more topics be covered in this course and let more people know how these state-of-art models work and how they really change the world.

by Xiang J

Nov 04, 2019

I really like this course, because it not only taught me the exciting new topics that I always want to learn, such as object detection algorithm and neural style transfer, but also it gave a solid introduction to the concepts of convolution. The assignments are great, it is fun to do and it also helped me more concretely understand the materials of main course. As to further improve the course, may be it would be nice to build a whole end-to-end pipeline including training the main convolution model in car detection as I know in Google colab even public users have access to GPUs.

by Mukund C

Oct 15, 2019

Loved it!! Loved it!! Loved it!! I wish there was a little bit more engagement from mentor side as well as updates on the coursework with the latest developments in the object detection field. I also wish that there were a little bit more involved programming exercises, maybe one in "training" where one has to label objects and "train" a neural net. One of the things that I missed in the course is an explanation of the Neural Network architectures and why they work - e.g. the VCCG-16 or Inception Network - for example. Maybe one has to read the papers to understand them?

by Shankar G

Jul 08, 2018

This part of the CNNs course in DL was awesome and long enough. It started with foundations of CNNs, where the concepts of CNNs layers was made very clear. Programming assignments helped understanding the layering activation properly. The good part was DeepCNNs case studies explanation with its pros and cons, plus the practical advice for using ConvNets. Also this course provided few papers applications like object detection, face recognition and neural style transfer which was amazing. All the quizzes and programming assignments refreshed the concepts in a good manner.

by Mahmoud s m

May 23, 2020

i hope we could implement every code from scratch , i mean that you don't do the heavy lifting for us and we start the code from the zero point no matter how much time or effort it would take us , implementing codes in the existing manner is great , but creating it and passing through all phases of the code like arranging the code , efficiency in programming , the steps of writing a certain function also the arrangement of all functions like(which before which) .All of this will help us gain better hands on programming ourselves . thx for the great course :D :D

by Abhilash V

Apr 19, 2018

This course covers the basics of convolutional neural networks , resnets, inception nets, yolo, style transfer, face recognition.The programming assignments mostly for yolo and face recognition is done with transfer learning , i think its only fair as they are computationally expensive to train.I am confident about all the materials covered in this course Andrew Ng as always breaks down the problem to the basics so you can understand them.Its a great course if you want to know and implement the well known computer vision problems with the well known algorithms.

by Alouini M Y

Dec 26, 2017

This course helped me consolidate my computer vision knowledge. In fact, I had some prior experience but felt left behind given the current rapid advancements in the field of computer vision (thanks to deep learning mostly). The material is up-to-date and the assignments (especially the notebooks) are very pleasant. I have learned a lot of modern CV techniques: YOLO for image detection and localisation, style transfer, face verificiation with DeepFace, and many more. I recommend to anyone that is serious (or at least curious) about modern CV techniques.

by Jeffrey S

Apr 10, 2018

I had a tough time on the programming exercises - mostly due to poor Python/Numpy/Tensorflow experience. I did find the material really interesting. The teaching style is great - much better than other courses on AI I've started. Andrew is terrific and pleasant to learn from. While totally different from the megastar CS50 (EdX) approach, he manages to make a complicated subject understandable. I have my list of subjects I need to go back and review, but I really feel like I've gotten a good perspective on the Deep Learning field from these courses.

by Jairo J P H

Feb 01, 2020

El curso es muy bueno, particularmente estoy muy agradecido con COURSERA, por darme la oportunidad de hacer los cinco cursos de la Especialización en Deep Learning con ayuda economica y permitirme tener acceso a este tipo de capacitacion y certificacion. Muchas Gracias…!

The course is very good, particularly I am very grateful to COURSERA, for giving me the opportunity to do the five courses of the Deep Learning Specialization with financial aid and allowing me to have access to this type of training and certification. Thank you very much!

by Martin K

Jan 15, 2019

Andrews unique way of presenting complex theoretical concepts in a compelling and easy to understand manner was essential for my learning success. Attending this course was fun. Even though the programming assignments were pretty tough in this course (for me the toughest of all the courses in the deep learning specialization), I managed to complete this course in (my) record time. This might be mostly due to the understanding of the underlying mathematical concepts which were outstandingly well presented.

Totally recommend this course!

by David A G

Mar 16, 2018

The course was excellent. I really enjoy Andrew Ng's courses: complex stuff made easy and lots of practical applications.

The only thing that I would try to improve is the time the staff dedicates to check the forum to solve student's questions. I personally got stuck at one of the quizzes and it was hard to find any clue that might help to understand the right answer. Also, some really interesting general questions on the forum were not replied by anyone. I'm sure some expert help on the forum would bring great value to the course.

by Marcel M

Jun 30, 2018

For an engineering discipline, there is nothing better than employing the latest state-of-the-art techniques in solving real-life problems. That's the inherent value of this course the fact that you learn how Deep Learning is having an impact on so, so.. many, diverse areas such as Self-Driving Cars, Object Detection, Localization, Classification, Verification, Recognition and much, more. I highly recommend this course to anyone who wants to be an adept Deep Learning Practitioner. Kudos! Team DeepLearning AI. Keep up the good work!

by J K

Feb 25, 2018

The best course (yet). A good balance between theory and practice, although the complete lack of TensorFlow and Keras fundamentals can be a bit frustrating. Additionally, the use of numpy operations (add, multiply and such) gave the impression that you'd correctly done a function assignment (the check values were OK), however, the grader failed to accept it as being correct (which was justified), however, an indication that it was incorrect (or some comments in the accompanying text) would've saved me 30 minutes of searching.

by Ahmed E S A H

Nov 13, 2017

This course is very good. But i hope, after the course's weeks end, to add one more section to explain the recent publications and the most important challenges in the course field. In my opinion, this section will help the researcher to find a path to start research in course topic and try to find a new contributions that can help them specially if there are new master's or PhD students, they can figure out quickly where to start there research topics.

Thank you for your great effort and i hope i can learn more via Coursera.