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Avis et commentaires pour l'étudiant pour Convolutional Neural Networks par

26,811 notes
3,234 avis

À propos du cours

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....

Meilleurs avis


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.


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.

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251 - 275 sur 3,195 Examens pour Convolutional Neural Networks

par Bayartsogt Y

Mar 05, 2019

This was such an interactive course. I hope many people should start learning this

par Shirish P

Mar 06, 2019


par Yehua Y

Mar 06, 2019

Wonderful lessons, i am benifit a lot from the lessons, thanks.

par 荣灿

Mar 03, 2019


par Nikesh P

Mar 02, 2019

From basics of a Convolutional Neural Networks to the applications of CNN have been taught very well.

par Mallikarjun C

Mar 01, 2019

Excellent course


Mar 03, 2019

This course was the best I have ever taken. It gave me a big boost to carry my PhD research in robot vision with confidence of understanding what is happening all over the network and comprehension of one of the pioneer papers published in discussed in classes. Coding directly after finishing each week was the best to go to practice and apply all this knowledge gained.

par Onur G

Mar 03, 2019

Great introduction to deep learning! I recommend this course to everyone

par Vignesh K

Mar 04, 2019

Extremely useful course for image analysis - classification as well as object detection along with style transfer, etc. Also very useful for Tensorflow novices.

par Andreea A

Mar 03, 2019

The course has a lot of good content and the programming assignments are interesting. The course actually describes the various architectures of CNN's and the reasoning behind them. It still has some video editing issues.

par Matei I

Mar 03, 2019

A lot of quality content in this course. The first half focuses on the intuition behind ConvNets and their implementation, while the second half focuses on applications. I thought that the neural style transfer application was particularly enjoyable. My only suggestion for improvement is to let the students do more work in the assignments for the last two weeks. I feel that most of the code in these assignments was black boxed, and I got to implement a minimal portion of the algorithms.

par SAI V K

Mar 05, 2019

Excellent course for computer vision techniques, must recommended

par Maxim M

Mar 04, 2019

This course explains all magic behind the scenes of computer vision.

No magic - only math and tons of data ;)

par Qiongxue S

Mar 04, 2019

I learned a lot from this CNN course, notations, algorithms, tensorflow and keras application. I would strongly recommand to learn this course. It made me think a lot smart applications in daily life and know better about what artifical intelligence is. Of course this is far more than enough, and I will keep learning the related knowledge and reading more about NN. Thanks a lot for the excellent tutorial!

par Juilee D

Mar 06, 2019

Awesome course

par Sebastian S

Mar 06, 2019

THe notebooks are sometimes not very intuitive. Overall course really good.

par Daniel M G H

Mar 09, 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.

par Jizhou Y

Mar 08, 2019

Professor Andrew is really knowledgeable. The lecture videos he makes are really helpful for me. I really learn a lot from them. Also, the recommended learning materials such as academic paper he recommend are really useful for me if I want to further my learning on the residual network or YOLO algorithm.

par Ahmed M

Mar 07, 2019

Thanks a lot. I learned a lot of things from these courses

par Vardhman K

Mar 08, 2019

Outstanding content and it's presentation is remarkable.

par Dharanidaran

Mar 10, 2019

I gained the ability to read and understand research papers after taking this course. I you want to have a good course on Object Detection, Neural Style transfer, I would highly recommend this course.

Thanks Andrew.

par Camilo G

Mar 10, 2019

Amazing course

par David R V O

Mar 11, 2019

I think this course is excellent and I'm already applying the skills I've learnt from it to my current research. I would have preferred a little bit more focus on the theorical part of ConvNets, especially backprop. 100% recommended.

par kazım s

Mar 11, 2019

Course was well structured, and easy to follow. It also covers recent developments and famous papers, which was the best part for me. Many thanks to Coursera and prof. Ng for preparing and teaching us such valuable materials.

par Mersa L N

Mar 11, 2019

Interesting material. ranging from basic to advanced. This course is equipped with exercises and examples that are easy to understand.