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Convolutional Neural Networks(으)로 돌아가기

deeplearning.ai의 Convolutional Neural Networks 학습자 리뷰 및 피드백

4.9
stars
27,845개의 평가
3,357개의 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

RK

Sep 02, 2019

This is very intensive and wonderful course on CNN. No other course in the MOOC world can be compared to this course's capability of simplifying complex concepts and visualizing them to get intuition.

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.

필터링 기준:

Convolutional Neural Networks의 3,318개 리뷰 중 226~250

교육 기관: Luis A O A

Mar 20, 2018

I had only a little knowledge of CNN and struggled to grasp some concepts but after watching the lectures only once I can confidently explain the structure of a CNN and even compute the dimension of the layers on the fly thanks to the quiz questions. Totally would recommend.

교육 기관: Fanyi D

Nov 18, 2019

Prof. Andraw Ng is very good at presenting the core ideas to audience in simple and intuitive words and this course is especially useful for engineers with different background to step into or refresh some principles of the CNN. I personally strongly recommend this course.

교육 기관: Kurt K

Nov 27, 2017

A clear explanation of a difficult subject with an emphasis on being able to create and to understand your own neural networks.- Plus in this module how to use algorithms which significantly reduce your computational needs and with an introduction to processing visual data.

교육 기관: Arkajyoti M

Jun 10, 2019

Thank you so much for this wonderful course.

I have only one suggestion there's a lot of bugs in the notebooks, especially the last one Week 4 Happy House Face recognition. Please fix that as a lot of weights are missing and completing that exercise involves a lot of hacks.

교육 기관: Alex B

Oct 12, 2018

The most challenging course in the series so far, it was also the one that helped me best understand how these networks function. I have already recommended this course to colleagues, and think it is the perfect course for an intro to computer vision, tensor flow and Keras

교육 기관: Aditya A G

Mar 17, 2018

Very nicely prepared and presented. Assignments gives good insights into concepts learned while for yolo,neural styles, face recognition problems eagerly looking for building CNN architectures from scratch & training them in future courses. Thanks a lot Andrew & Team..!!

교육 기관: Serzhan A

Nov 20, 2017

The best course in the series so far. Andrew Ng makes the complicated seem easy and does so by dividing the topics into small digestible pieces. You will binge-learn his courses because of how addicting and gratifying the experience of learning is made by the instructor.

교육 기관: Leonardo R C

Jun 01, 2019

This is a very interesting and fun course to take. You put into practice all the knowledge from previous coruses from the specialization and apply them in applications that are changing the world right now. As usual, professor Andrew explains every concept perfectly.

교육 기관: Harold L M M

Nov 19, 2018

The best course by far in this specialization. This course covers all the important topics in Convolutional Neural Networks, face verification and face recognition.

You have to work very hard to complete it. Thus, it's a great challenge!

Thank you Professor Andrew Ng!

교육 기관: Yoan S

Oct 05, 2018

These courses are VERY well put together and concentrate excellent concept in little time compared to taking the available Stanford CNN classes online which are verytime consuming for the same result. Andrew is motivating and makes difficult concepts very accessible.

교육 기관: Xavier S P

Dec 20, 2017

The idea of inserting convolutions into the net and in the back propagation is really cool yet so simple to implement after watching those lectures. It makes sense why image simplification via convolution in layers can greatly help performance in a deep learning net.

교육 기관: 梁礼强

Apr 02, 2019

this course is pretty good,but the some of these techniques introduced in class are slightly out-of-date,such as yolo v2 and this version of neural style transfer. It's OK as an introduction, but it may be better to mention the latest or general version algorithms.

교육 기관: Nilanka W

Jan 14, 2018

This course taught how the latest computer vision systems works. The content is really great and the lectures and mentors have put a lot of effort in creating the assignments and notebooks, which are high quality. recommend to anyone who are interested in the field

교육 기관: Rúben G

Oct 20, 2019

Through this course I understood how modern Computer Vision tasks are addressed with CNN. Also I learn that a CNN can be combined with a FCN. I further understand better the notion of the neural network and the advantage/disadvantage of having more or less layers.

교육 기관: Michael F

Nov 01, 2018

The best in this series of courses so far. The maths was hard, and the programming assignments were accordingly at a higher level. But the applications of ConvNets are so fascinating, and their implications so profound, that I enjoyed every moment of this course.

교육 기관: Pavan K V

Jan 19, 2018

the best course out of all 4 in deep learning.The best thing i liked most in this course is the applications such as

1) Image classification/Image recognition

2) Object detection-Automatic Car Driving

3) Face Verification and Face Recognition

4) Neural Style Transfer

교육 기관: Zhao Y

Nov 25, 2017

This course gives me a deep understanding of CNN and also introduces me some latest information about face recognition. It makes me have an access to learn AI in an efficient way. Words seem to fail me when I want to show my gratitude to the teachers and mentors.

교육 기관: khalid w

Nov 10, 2019

This course has helped me very much in understanding the nomenclature of convolution networks. Previously I struggled reading different research papers related to convolution networks as I was unable to understand the different dimensional changes in each layer.

교육 기관: Oleksiy S

Dec 20, 2018

Exellent course for first experience with convolutional networks. A few mistakes that seem frustrating at the time you are completing course really help to gain better overall understanding. Thanks a lot for good work all the involved people, stuff and mentors.

교육 기관: Sayar B

Aug 16, 2018

Perhaps the toughest course so far, Convolutional Neural Networks introduces us to computer vision. Professor Andrew explains complex, state-of-the-art cases where computer vision is being used today. Great programming assignments, great lectures, great course.

교육 기관: Shifeng X

Mar 25, 2018

awesome course! the assignment is actually not just a piece of homework, it indeed a kind of guidance, give you detail step by step examples of how to code the learned algorithm. Thanks to the lecturer, didn't find any course more 'user-friendly' than this one.

교육 기관: ABEL G G

Nov 07, 2017

Wow.. what Can I say? This was the toughest of the three previous but super happy to be in this journey.

I learned a lot and I am motivated more than anytime to immerse myself in this field. There is so much to learn. Thanks to all the people behind this course!

교육 기관: Karthikeyan R

Dec 29, 2019

Again, excellent course from Andrew Ng! Made complex algorithms and concepts very clear! Got to know how CNN, Facial recognition and Object detection works. Reference to the literature paper will come handy in the future if one thought of diving deep into CNN.

교육 기관: Julian S

Nov 20, 2017

Excellent course. Concepts very clearly described. Only improvement would be more Tensorflow and possibly Keras training. Yes, you can go elsewhere for this, but Andrew Ng is so good at explaining, I'd expect he'd do a better job!

Many thanks Mr Ng and team!!

교육 기관: Yao F

May 20, 2019

The course is pretty good with advanced techniques on computer vision. The only regret is one problem about the last coding homework. I failed to load the pre-trained model and can only finish the home work without checking the accuracy of designed examples.