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

4.9
42,557 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:

2101 - Convolutional Neural Networks 的 2125 个评论(共 5,642 个)

创建者 sarathva v

Jan 26, 2024

Course is really good but need some explanation to tensor flow coding .

创建者 Morteza M

Sep 24, 2022

perfeeeeeect.just if use pytorch rather than tensorflow is much better

创建者 Levin

Aug 7, 2022

Amazing course!

I get big intuition about Convolutional Neural Networks.

创建者 vinoth k

Dec 26, 2021

Best ever course for those who are in need of strong foundation in CNN.

创建者 Pasan J

Oct 4, 2021

Amazing course! Very detailed and explained as expected! Thanks andrew!

创建者 Rugved R K

Aug 3, 2021

Great course, Learning a lot of things, very good for beginners like me

创建者 sushil d

Jul 31, 2021

very helpful to understand different vision models with hands on coding

创建者 Salma H S

Oct 11, 2020

I wish the illustration of TensorFlow and Kares to go deeper than this.

创建者 Mr. R G

Aug 22, 2020

Gave practical insights in to popular convolution network architectures

创建者 David H

Mar 24, 2020

It's a great course to introduce into de Convolutional Neural Networks.

创建者 Sinan C

Jan 6, 2019

Outstanding course. Thank's a lot for the great information and effort.

创建者 Diego N

Sep 8, 2018

Simply explained very powerful state of the art convolutional networks.

创建者 Nguyen S A

May 29, 2018

very interesting content, detailed explanation and useful exercises too

创建者 Mayur V

May 14, 2018

Grader needs improvement...sometimes it won't submit my correct answers

创建者 Junheng Z

Mar 19, 2018

It is great, you will know CNN,VGG,YOLO2,.... so much model.

It is great

创建者 Jan Z

Mar 6, 2018

Very informative, concise, well presented, builds necessary intuition.

创建者 Yaseen L

Dec 11, 2017

Very comprehensive course on deep learning problems in computer vision.

创建者 Tuan N

Nov 22, 2017

It was a great course to implement some cool applications like FaceNet.

创建者 Evaldas B

Nov 11, 2017

Very good basics for CNN. Some knolege of Keras and Tensorflow aquired.

创建者 Manav G

Dec 22, 2023

Extremely well taught course and well designed programing assignments!

创建者 Tien P

Oct 30, 2021

It's great course with good videos and hand-on programming assignments

创建者 Safayet I

Dec 28, 2020

Learned a lot from the basics. Wonderful course. Enjoyed it althrough!

创建者 Sebastian J

Jul 21, 2020

Superb instructor fantastic course structure well designed assignments

创建者 Abdelkader B

May 27, 2020

a complete introduction to CNN, I enjoyed every exercise in this cours

创建者 Dhanya S

May 23, 2020

Very helpful course giving a pretty well rounded understanding of CNN.