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Вернуться к Convolutional Neural Networks

Отзывы учащихся о курсе Convolutional Neural Networks от партнера deeplearning.ai

4.9
Оценки: 22,976
Рецензии: 2,784

О курсе

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....

Лучшие рецензии

AG

Jan 13, 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.

EB

Nov 03, 2017

Wonderful course. Covers a wide array of immediately appealing subjects: from object detection to face recognition to neural style transfer, intuitively motivate relevant models like YOLO and ResNet.

Фильтр по:

1–25 из 2,758 отзывов о курсе Convolutional Neural Networks

автор: Gyuho S

Apr 25, 2019

This course is definitely tougher than the first three courses. Challenging but worth it.

автор: David B C S

Dec 17, 2018

Great course, easy to understand and very useful. The explanations are very clear, as is expected from the professor. The purpose of the course is for you to have a practical comprehension of CNNs, it will give you the necessary tools to implement you own networks, but it will not get into the specifics of each model. Nevertheless, all of the resources are referenced, which makes it very easy for you to dig deeper on any specific topic covered on the course.

автор: Aleksa G

Jan 13, 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.

автор: Farzeen H

Jan 12, 2019

Amazing! Feels like AI is getting tamed in my hands. Course lectures , assignments are excellent. To those who are not well versed with python - numpy and tensorflow , it would be better to brush up.

автор: fabrizio f

Dec 17, 2018

Very good however most of the effort is applied in learning and applying programming (tf, Keras) than actually thinking about the DL models and practicing different scenarios.

автор: Sergei S

Apr 29, 2019

Some parts of the course seemed incomplete to me, wanted more information on why things work exactly as described. Last week assignments have a number of uncertainties/bugs.

автор: Ralph J R F

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

автор: 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.

автор: Huijun P

Apr 18, 2019

Great lectures but the programming assignments feel as if it is testing your proficiency with tensorflow which is neither formally covered in the lecture nor the most intuitive framework to understand so you'll spend so much time digging through convoluted tensorflow documents and qna and whatnot to debug your codes that you would rather learn tensorflow formally first and then take this course and still end up finishing it faster than only going through this course only but it is only the programming assignments that basically assume that you are already familiar with the tensorflow framework so if you are only going to go over the video lectures it gives a great overview of how CNN works and many useful algorithms which can applied to a assortment of situations

автор: divya p p

Feb 18, 2019

Dear Instructors,

This is most frustrating course in all of your courses so far. The instructions were completely misguiding the candidates from YOLO implementation onwards. All along you presented the course very well. But when come to most important topics, we had to focus on syntactical errors. But we are supposed to spend time on understanding the algorithms at this level. Dont know why this 180 degrees turn taken by you. If you intentionally designed this course then fine. Otherwise, you should seriously think about rework on the instructions. Few links to hints were taking to some pages in github with just folders.

I am sure , many learners here have such same opinion. I can see this in the forum postings.

From YOLO onwards, you were not giving the big picture of the task. This is confusing. We are lost, where we are heading by the mid of the assignment.

With all due respect to your highly precious time, I request you to enhance the assignment instructions.

All motivation I got from previous course, losing because of this course.

Personally, I feel YOLO easy to understand, but instructions were misguiding and confusing the candidates.

This is my honest feedback, as I very much like this course. I am going forward for the 5th course in this series.

Last but not least. Thank you for making this high quality knowledge made available for public with easy access via Coursera.

автор: Xinwei B

Feb 13, 2019

When I am doing the programming assignments, I felt that some part were quite difficult since I had no background in neither Keras nor Tensorflow. It was helpful that in one of the previous courses there was a tutorial for the basics of Tensorflow. But for Keras I felt that there is a gap between what I have and what is needed for the assignment. So I would suggest a more thorough tutorial for Keras. Maybe several short tutorials talking about the implementations and ideas of Tensorflow & Keras may help a lot.

автор: Sriram G

Feb 10, 2019

Homeworks are too canned and do not promote deeper understanding.

автор: Alberto B

Feb 08, 2019

Assignments are very bad explained

автор: Cosmin D

Jan 04, 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.

автор: Michael J

Jan 02, 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.

автор: Tian Q

Jan 01, 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.

автор: Stefan J

Dec 30, 2018

Theoretical material was great as always. However, programming assignments were poorly commented in some cases which results in unnecessary confusion.

автор: Markus B

Dec 05, 2018

Great course. The only improvement I'd wish is to get a better introduction to the concepts of Tensorflow and Keras.

автор: 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 :

$$J_{style}^{[l]}(S,G) = \frac{1}{4 \times {n_C}^2 \times (n_H \times n_W)^2} \sum _{i=1}^{n_C}\sum_{j=1}^{n_C}(G^{(S)}_{ij} - G^{(G)}_{ij})^2\tag{2} $$

What append ? that was great so far... =(

автор: Bhuvan P K

Jul 22, 2019

A very comprehensive course on convolutional neural networks and their applications in the computer vision domain. The last weeks content on Neural style transfer was also useful and interesting

автор: Avaneesh

Jul 22, 2019

It was a pretty good experience although i faced some problem in one of the assignments.

автор: Ashwin m

Jul 22, 2019

very good topics discussed ,facial recognition and facial verification assignments do not do justice to the complexity involved.practical knowledge gained is less compared to other modules prior to this.

автор: Yujie L

Jul 22, 2019

The course is great, but it's almost impossible to learn in China without a vpn. Even if I got a VPN, I cannot connect to the jupyter notebook stably. And the course project is quite easy..

автор: Jack S

Jul 21, 2019

Great course! I learned so many stuff. Andrew's lectures are very intuitive and helpful. Those Jupyter notebooks are definitely worth time exploring. One last thing is that I wish some limits of the current CNN model can be mentioned for big picture.

автор: Balu P

Jul 20, 2019

This was a great course which helped me go through the concepts of CNN and how it's used as a beginner.