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

4.9
42,537 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:

5076 - Convolutional Neural Networks 的 5100 个评论(共 5,640 个)

创建者 Pierrick R

Feb 27, 2021

I think that the exercices are really easy, you have to change this part.

创建者 Ahouba C A A

Jun 24, 2020

I wish there had been more explanations something regarding propagations.

创建者 Kalyan A

May 2, 2020

one less star because you are not giving me any material for this course.

创建者 MEKALA S N

May 1, 2020

Overall course was good. Some videos are lengthy, people might get bored.

创建者 Christian A

Feb 25, 2019

excellent course. the jupyter notebooks were behaving erratically though.

创建者 Amit K

Apr 13, 2018

this one was hard to clear thanks for wonder full tutorials and questions

创建者 Fereydoon V

Feb 26, 2018

Tensorflow and Keras tutorials need improvement and further explanations.

创建者 Benjamin H D

Dec 10, 2017

Material is great as always, the audio could certainly be improved though

创建者 Katharina E

Jan 9, 2021

Content is great, but the auto grader has issues costing a lot of time!

创建者 Michael F

May 24, 2018

Programming assignments are too easy, consisting largely of copy&paste.

创建者 Richard Y

Feb 25, 2018

Very good course. Just please fix the buggggggggy grader in the week 3.

创建者 Pengbo L

Jan 24, 2018

The last assignment on triple-loss has the grader-error, which a couple

创建者 BAZIZ M

Aug 4, 2023

I would've given it 5 stars if the assignements were more challenging

创建者 Dino P

Jul 2, 2020

I'd have given it 5 if programming exercises were modified to use TF2.

创建者 Ukachi O

May 10, 2020

A wonderful introduction and implementation to the concepts of CovNets

创建者 Vamvakaris M

Sep 8, 2019

It required coding on keras and tensorflow not appropriete introduced.

创建者 Emmanuel R

Jun 10, 2018

Very hard at week 2. Week 3 and Week 4 were very exciting. I liked it.

创建者 Clay R

Feb 21, 2018

The grader could use some more debugging but otherwise excellent work.

创建者 David P

Dec 6, 2017

Great course! Assignment notebooks could be a bit more challenging...

创建者 Shin-Young H

Dec 12, 2024

The propgramming part would be better if not to use a trained model..

创建者 Rafael G M

Jan 26, 2020

Good material.

I recommend explaining YOLO with more conceptual depth

创建者 Jaisuthan A

Sep 2, 2020

Nice course. But we are not working on latest versions of Tensorflow

创建者 Jinfeng X

Nov 12, 2019

Great content! It would be better if some missing slides were there.

创建者 龚华君

Jul 23, 2019

Neural style transer part is hard to understand, the rest part easy

创建者 Lin Z

May 7, 2019

interesting introduction materials on convolutional neural networks.