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Convolutional Neural Networks(으)로 돌아가기

deeplearning.ai의 Convolutional Neural Networks 학습자 리뷰 및 피드백

4.9
24,040개의 평가
2,903개의 리뷰

강좌 소개

This course will teach you how to build convolutional neural networks and apply it to image data. Thanks to deep learning, computer vision is working far better than just two years ago, and this is enabling numerous exciting applications ranging from safe autonomous driving, to accurate face recognition, to automatic reading of radiology images. You will: - Understand how to build a convolutional neural network, including recent variations such as residual networks. - Know how to apply convolutional networks to visual detection and recognition tasks. - Know to use neural style transfer to generate art. - Be able to apply these algorithms to a variety of image, video, and other 2D or 3D data. This is the fourth course of the Deep Learning Specialization....

최상위 리뷰

AG

Jan 13, 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.

EB

Nov 03, 2017

Wonderful course. Covers a wide array of immediately appealing subjects: from object detection to face recognition to neural style transfer, intuitively motivate relevant models like YOLO and ResNet.

필터링 기준:

Convolutional Neural Networks의 2,870개 리뷰 중 251~275

교육 기관: Nikesh P

Mar 02, 2019

From basics of a Convolutional Neural Networks to the applications of CNN have been taught very well.

교육 기관: Mallikarjun C

Mar 01, 2019

Excellent course

교육 기관: WALEED E

Mar 03, 2019

This course was the best I have ever taken. It gave me a big boost to carry my PhD research in robot vision with confidence of understanding what is happening all over the network and comprehension of one of the pioneer papers published in discussed in classes. Coding directly after finishing each week was the best to go to practice and apply all this knowledge gained.

교육 기관: Onur G

Mar 03, 2019

Great introduction to deep learning! I recommend this course to everyone

교육 기관: Vignesh K

Mar 04, 2019

Extremely useful course for image analysis - classification as well as object detection along with style transfer, etc. Also very useful for Tensorflow novices.

교육 기관: Andreea A

Mar 03, 2019

The course has a lot of good content and the programming assignments are interesting. The course actually describes the various architectures of CNN's and the reasoning behind them. It still has some video editing issues.

교육 기관: Matei I

Mar 03, 2019

A lot of quality content in this course. The first half focuses on the intuition behind ConvNets and their implementation, while the second half focuses on applications. I thought that the neural style transfer application was particularly enjoyable. My only suggestion for improvement is to let the students do more work in the assignments for the last two weeks. I feel that most of the code in these assignments was black boxed, and I got to implement a minimal portion of the algorithms.

교육 기관: SAI V K

Mar 05, 2019

Excellent course for computer vision techniques, must recommended

교육 기관: Maxim M

Mar 04, 2019

This course explains all magic behind the scenes of computer vision.

No magic - only math and tons of data ;)

교육 기관: Qiongxue S

Mar 04, 2019

I learned a lot from this CNN course, notations, algorithms, tensorflow and keras application. I would strongly recommand to learn this course. It made me think a lot smart applications in daily life and know better about what artifical intelligence is. Of course this is far more than enough, and I will keep learning the related knowledge and reading more about NN. Thanks a lot for the excellent tutorial!

교육 기관: Juilee D

Mar 06, 2019

Awesome course

교육 기관: Sebastian S

Mar 06, 2019

THe notebooks are sometimes not very intuitive. Overall course really good.

교육 기관: Daniel M G H

Mar 09, 2019

Very good content, but assignments have minor issues when sending for grading. These issues shouuld be pointed because sometimes the answer is correct but the grammar not and this is evaluated as a 0 grade.

교육 기관: Jizhou Y

Mar 08, 2019

Professor Andrew is really knowledgeable. The lecture videos he makes are really helpful for me. I really learn a lot from them. Also, the recommended learning materials such as academic paper he recommend are really useful for me if I want to further my learning on the residual network or YOLO algorithm.

교육 기관: Ahmed M

Mar 07, 2019

Thanks a lot. I learned a lot of things from these courses

교육 기관: Vardhman K

Mar 08, 2019

Outstanding content and it's presentation is remarkable.

교육 기관: Dharanidaran

Mar 10, 2019

I gained the ability to read and understand research papers after taking this course. I you want to have a good course on Object Detection, Neural Style transfer, I would highly recommend this course.

Thanks Andrew.

교육 기관: Camilo G

Mar 10, 2019

Amazing course

교육 기관: David R V O

Mar 11, 2019

I think this course is excellent and I'm already applying the skills I've learnt from it to my current research. I would have preferred a little bit more focus on the theorical part of ConvNets, especially backprop. 100% recommended.

교육 기관: kazım s

Mar 11, 2019

Course was well structured, and easy to follow. It also covers recent developments and famous papers, which was the best part for me. Many thanks to Coursera and prof. Ng for preparing and teaching us such valuable materials.

교육 기관: Mersa L N

Mar 11, 2019

Interesting material. ranging from basic to advanced. This course is equipped with exercises and examples that are easy to understand.

교육 기관: Michael O

Mar 10, 2019

Extremely insightful and great eye-opener: I've been able to come into a good basis for understanding the theory and practice behind usage of CNN's.

교육 기관: 陈雪松

Mar 12, 2019

I learn a lot from this serial courses, especially from exercise.

교육 기관: Bogdan G

Mar 12, 2019

Excellent course on CNN which gets you familiar with many popular models from 2012-2016 including all the basic CNN models LeNet, AlexNet, VGG, Inception, ResNet, object localization/detection and YOLO, Face recognition with DeepFace, etc. Thanks!

교육 기관: SW J

Mar 13, 2019

It's very helpful to understand cnn and its apply to face recognition. Thank you.