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Learner Reviews & Feedback for Convolutional Neural Networks by DeepLearning.AI

4.9
stars
42,318 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

OA

Sep 3, 2020

Great course. Easy to understand and with very synthetized information on the most relevant topics, even though some videos repeat information due to wrong edition, everything is still understandable.

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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926 - 950 of 5,613 Reviews for Convolutional Neural Networks

By Aditya K

Apr 23, 2020

One of the best course available on the subject. I personally love the teaching style of Sir Andrew NG, Thank you for making learning easy and so interesting.

By Shantanu A

Jan 14, 2020

I enjoyed doing this course and learned so much in each lecture! Assignments were incredibly fun and loved implementing such cool papers. Thank you very much!

By 蕭博偉

Jan 13, 2020

Well to understand a basic CNN and its application. Also introduce several CNN like LeNel, AlexNet...etc. to illustrate what is going on in each hidden layer.

By Meera J K

Dec 18, 2019

Amazing course...I think every deep learner :-) should take this course. Powerful, clear and insightful teaching of Andrew Ng Sir. kudos to the team behind...

By Bishwaraj D

Aug 27, 2018

This course so far has been the most interesting as well as challenging. The assignments seemed a bit difficult because I wasn't very skilled with TensorFlow.

By Sergio B S

Aug 17, 2018

Very difficult, very deep... but very enligthening

(Totally Recommended, although working with images may not be your main objective with deep learning... :-)

By Dario R

May 21, 2018

Awesome Professor, awesome Mentors, awesome material. This course is so amazing. I enjoyed it a lot!!! Keep the good work. I love Coursera and DeepLearning.ai

By Jeroen M

Feb 13, 2018

Brilliant course. There are a few small problems with some of the assignments, but overall I've learned so much that these small imperfections are irrelevant.

By Nacho C

Nov 30, 2017

Good introduction to the fascinating area of Convolutional networks. Great overall, but finishes on a low note with the trivial Neural Style Transfer section.

By Rohit K

Dec 5, 2022

Exceptional course on CNNs. Programming assignments proved to be very useful to see for oneself the incredible power of advanced CNNs taught in the lectures.

By Dhruv S

Jun 22, 2020

The most broad topic in the entire specialization. The practical applications through programming assignments reinvigorates the theory thought in the videos.

By Varun N G

Apr 17, 2021

Very nice course, the instructors have explained difficult concepts right from scratch and adequate depth which is required for implementing these networks.

By Amit M

Mar 27, 2021

Relatively easier course. Good intuitive understanding and can do the homeworks easily. But not sure if the homework could have been done without the hints.

By Hamza J

Jun 16, 2020

By far the best course in this series. honestly i got to know so many new things that i have not learned anywhere ... thanks andrew you are the best teacher

By Taiki O

May 14, 2020

Absolutely awesome. The YOLO algorithm was a little bit hard for me but I was saved by the well-organized lecture and the programming assignment. Thank you.

By A H

May 13, 2020

the course was great, gave me all the perspective i was looking for to understanding the convolution NN. more explanation on the YOLO would have been better

By Mohsin T

Sep 15, 2019

Its one of the best courses i have taken, it help with my current research of crop detection for developing countries using multispectral satellite imagery.

By Deleted A

Jul 9, 2019

Excellent course to learn intuition about CNNs. A tip for new students, brush up on tensorflow and keras before jumping into this to maximize your learning.

By Abdallah M

Jan 16, 2018

You have to fix the problems with week 4 assignments. If you follow the instructions on "triplet loss" the grader will give you a wrong answer and 0 grades.

By Jennifer R

Dec 10, 2020

This course is a little challenging, but 100% worth it! Wow. I learned things I never thought I could. The examples, analogies, and assignments are superb.

By Jiying L

Sep 29, 2020

Classes are well taught, and key concept are clearly explained. The exercises are not difficult but give a good sense of CNN concepts and its applications.

By Shaun Z

Apr 24, 2020

This course is fantastic. The intuitions given by the instructor are very clear and help a lot in visualising the process going on while implementing CNNs.

By Daniel T

Feb 26, 2020

Very good and detailled course. Programming exercises are however a bit too short and sometimes multiple ways of programming can lead to different results.

By Badr S

Feb 20, 2020

As always, Andrew is absolutely amazing at transmitting advanced knowledge and making it accessible to most people. Thank you Andrew and the whole team !

By Nikita S

Jan 5, 2020

Very elaborate concepts explained in a very simple and understanding manner. Many application points are covered, along with explaining research prospects.