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

25,557 notes
3,076 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


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.


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.

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151 - 175 sur 3,036 Examens pour Convolutional Neural Networks

par Mukund A

Feb 19, 2019

It was a really good experience. Best course available online. Well structured and well guided assignment. Got to learn a lot. Thank You!

par Manpreet M

Feb 19, 2019

Splendid course with extremely useful content and exercises. After this course you will definitely be comfortable with CNNs.

par 锐新华

Feb 19, 2019

very good course

par Somex K G

Feb 20, 2019

this course help me to understand how ConvNet works and how can we implement it in various ways.

par Qasid S

Feb 20, 2019

Great Course!!!

par Sharath G

Feb 21, 2019

Made my concepts clear on Computervision.

par Vidit G

Feb 22, 2019

very helpful

par TanBui

Feb 23, 2019

Very good indication of CNN. However, some of the assignment materials such as Keras needs prior experience which are not presented in the course.

par Xiao W

Feb 24, 2019

Very helpful and introductory

par Beng C C

Feb 24, 2019

Excellent course!

par Virginia

Feb 24, 2019

The course is a perfect balance between theoretical explanations, application in programming and tips that can be helpful if you intend to work with CNN. I had not seen CNN before, and I didn't feel lost at any moment. Every doubt I had was perfectly answered in the forum. You don't need much of an experience with TensorFlow or Keras to do the labs, which are accompanied by thorough explanations of what is required; on the other hand, there are "extra" tasks for people who want to go more into depth in each lab.

par 林业雄

Feb 24, 2019

very good

par Yan-Jen H

Feb 25, 2019

Nice course :)

par 介阳阳

Mar 20, 2019

Excellent teaching! Not access to the assigment yet but already feel so excited!

par LUCA D

Mar 21, 2019

Assignments are more challenging!

par Khor H Q

Mar 21, 2019

very fundamental and guided well.

par Vishnu N S

Mar 20, 2019

very good course and great content

par Jimut B P

Mar 21, 2019


par Akash G

Mar 20, 2019

The course was not in detail in terms of math and concepts. Like ML Course

par Mahima m

Feb 27, 2019

Amazing course work with very good content.

par Adam D

Feb 27, 2019

Andrew Ng explains thoroughly the state-of-the-art in object detection. Thank you!

par Vikas K

Feb 27, 2019

thank you andrew for thing great knowledge sharing .

par Reza

Mar 23, 2019

This course is easy to follow, cleared some black boxes for me. Even if you are not planning to purchase, it allows you to learn the most out of it by providing answered project assignment.

par jason z

Mar 24, 2019

Very good topic.

Lesson 3 and lesson 4 can be improved or separate into new course with more depth.

par Prajwal S N

Mar 24, 2019

Outstanding programming assignments. Well guided code. Concise lectures. Worth the time spent.