Chevron Left
返回到 Convolutional Neural Networks

学生对 DeepLearning.AI 提供的 Convolutional Neural Networks 的评价和反馈

4.9
42,558 个评分

课程概述

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....

热门审阅

SH

Aug 5, 2019

Great content in lectures! Automatic graders for programming assignments can be tricky, and set to old versions of tf sometimes, but answers to these issues are readily found in the discussion forums.

FH

Jan 11, 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.

筛选依据:

2126 - Convolutional Neural Networks 的 2150 个评论(共 5,642 个)

创建者 Luwei Y

Aug 30, 2019

Very detail lecture. Very helpful for understanding the basics of CNN.

创建者 Krishan B

Aug 4, 2019

Very Useful for understanding basics and advance concepts used in CNN.

创建者 Manudeep R

Mar 13, 2019

Best Course to get into the world of Computer Vision.

Great Instructor!

创建者 Den R

Jan 5, 2019

interesting lectures but coding assignments are boring and far fetched

创建者 Michael P

Sep 11, 2018

It started slow but the applications at the end were very interesting.

创建者 Carlos d H

Apr 9, 2018

Enjoy the examples. Prepare your own GPU to accelerate the examples ;)

创建者 LIYufei

Mar 10, 2018

Very nice. It would be even better with more image processing modules.

创建者 Tao Z

Mar 10, 2018

Best course about Convolutional Neural Networks, strongly recommended.

创建者 Rony C

Dec 10, 2017

last assignment could have been better setup. But good course overall.

创建者 Robert T

Nov 11, 2017

One of the more important of the courses in this great Specialization.

创建者 Farzad E b

Sep 18, 2022

It was a great experience! Everything were explained in the best way

创建者 Arturo G

Aug 19, 2022

Is a good option to learn cnn with theory and practical activities :)

创建者 MOHIT Y

Jan 29, 2022

It is the right course for beginning your journey in computer vision.

创建者 Muhammad A

Aug 14, 2020

Best course of the specialization so far. A lot of learning outcomes.

创建者 Chandra Y

Aug 7, 2020

Great insights into NN behind self driving cars and face recognition.

创建者 MIGUEL S I

Jun 29, 2020

I really liked the course. I learned a lot about convolution networks

创建者 Thierry L

Jun 22, 2020

I think , it could be interesting to merge course and coding session.

创建者 Cem G

May 15, 2020

Great course to understand the way CNN computer vision works. Thanks!

创建者 Fahim F

Apr 21, 2020

Excellent instructor, but the programming assignments are very tough.

创建者 Clément P

Apr 20, 2020

Really nice course. Everything's well explained and well illustrated.

创建者 Jose P

Feb 18, 2020

It took me a while to sit down, but honestly, this is a great course.

创建者 Vladimir Z

Oct 6, 2019

The hardest course of all. 4D matrices blown my mind ;) but I made it

创建者 Dawood h

Jun 9, 2019

highly recommended for computer vision and image processing in ML Way

创建者 Aman K S

Feb 9, 2019

Covers Computer vision from the basics.One of the best courses so far

创建者 Devang S P

Oct 8, 2018

Very well designed to produce it competent for development induction.