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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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4726 - 4750 of 5,570 Reviews for Convolutional Neural Networks

By Rangappa N

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Aug 10, 2020

It would be more helpful and beneficial if there is a deeper explanation for the network architectures involving the forward propagation, backward propagation and optimization

By Mark G

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Aug 8, 2019

Another very good course, only marred by the problems with running the final 'Happy House' face recognition notebook of week 4: the values did not seem to load into the model.

By Vinicius J S

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Aug 28, 2018

The course is good but the grades has severa errors - I spent a lot of time to understand what was wrong with my submissions - and was grade problem - consult always de forum.

By ashu a

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May 20, 2020

the course was good but the implementation of the code for style transfer could've been better it was a bit confusing apart from that the course was good and well constructed

By Vikram S

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Dec 29, 2020

Amazing course, providing a lot of insights in state of the art technology. One advantage might be to provide more hand-on in the programming exercises than just.a overview

By Giuseppe C

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Oct 25, 2019

Very good content and learning value. I would have given it 5 stars were it not for glitches I encountered in the material available including some of the Jupyter notebooks.

By Aakash G

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Mar 4, 2019

Completed some interesting assignments. Concepts are succinctly explained.

I think deploying these solutions in a real-world environment will show how well I have learned it.

By AKANSH M I L S

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Sep 29, 2019

The course is great no doubt however, after completing the course I still don't feel confident in writing code in tensorflow and Keras. Need practice to improve my fluency.

By Jerome B

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Jan 5, 2018

Very interesting content. Exercices are getting a bit messy, sometimes it's just not based on what has been learned coding-wise, sometimes there are issues with the grader.

By Steven S

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Nov 29, 2017

Phenomenal content, but frustrating problems with Jupyter Notebook. A determined programmer will get through those kinds of problems, but it definitely burned up some time.

By Daniel R B

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Jul 25, 2018

In content this is a 5 starts course but, there are issues grading the exercises that have being unfixed for quite a while... I hope they'll get fixed for future students.

By Arun p

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Dec 5, 2017

Nice Course on convolutional Neural Network. They could have used only one framework to complete the course assignments instead of switching between tensorflow and keras.

By henrik s

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May 3, 2020

Very good and well structured lectures. The assignments make you work through some very interesting problems, but there is not a lot room for creativity in the solutions.

By Francisco S

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Feb 23, 2020

I learned a lot. The tensorflow methods are not explained well enough (even if you did the previous courses of this specialization), and it still uses tf1 instead of tf2.

By Sven K

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Jun 7, 2019

All good, but something is amiss with the submission of the last week's Face recognition notebook. Use manual "Create submission" under "My submission" and then it works.

By Dmitry

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May 16, 2019

Great content. 2 hw exercises have some technical issues. E.g. hw4 Face Recognition doesn't teach much content but has a lot of bugs wasting time on it more than needed.

By vishnu v

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Dec 9, 2017

Great course, I would have liked it better if assignments were bit more difficult and also could have dig deeper into fewer CNN techniques rather than skimming over many.

By Shilin G

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Jul 26, 2019

Great course. Kind of getting difficult at week 3-4, especially week 4. Probably needs to be more familiar with TensorFlow in order to handle the programming assignment.

By Haim K

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Aug 7, 2020

Interesting materials. Give a good understanding of the concepts of constitutional networks.

week 4 is the weakest of the 4 weeks especially the programming assignments.

By Rocco I

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May 16, 2020

Challenging course but very interesting. It gave the opportunity to understand better what a neural network is doing (from a visual point of view). Thanks Professor Ng.

By Dennis K

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May 7, 2021

Create explanations in the vids! The programming exercises felt a little less refined compared to the previous courses, although I've to admit the bar was raised high.

By Andres A

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Mar 24, 2020

Andrew is an excellent teacher really. Quiz and Programming exercise were helpful to check the understanding on the topic. Overall it is a really good course to take.

By Zhan S

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Jan 16, 2018

This course explains very well how to use convnet, but however, I am a little disappointed because it does not explain why the convnet works and how to make it better.

By P C

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Nov 28, 2023

I believe the coding tasks could be a little more open and require back referencing as I felt I was able to do them without fully udnerstanding the subject at times

By Bishwarup B

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Jun 28, 2018

A little more explanation on the advanced models like object detection would have been very helpful. Also, semantic segmentation has not been covered in the module.