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Convolutional Neural Networks(으)로 돌아가기

deeplearning.ai의 Convolutional Neural Networks 학습자 리뷰 및 피드백

4.9
별점
31,706개의 평가
4,003개의 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.

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.

필터링 기준:

Convolutional Neural Networks의 3,963개 리뷰 중 76~100

교육 기관: Vincent F

Feb 08, 2018

Overall a very good course for the instruction. Found only two omissions with the programming assignment notebooks. One was where a function expected a tensor but the parameter we were encouraged to provide was an array. Had to use a convert to tensor call. The other was a mismatch between the expected output block and the grader. This has been noted already but has not yet been fixed. But quite minor all in all.

Really liked the links to the academic papers that are the source of the models used. Thanks again.

교육 기관: Maximiliano B

Jan 02, 2020

In this module of the specialization, you will be familiar with several types of convolutional neural networks and how do they work in details. Compared to the previous modules, this one requires more time due to the complexity of the subject as well as the programming assignments that are more difficult. After this course you feel comfortable to read all the papers covered as references throughout the course . Moreover, Professor Andrew NG explains the content clearly and it is a pleasure to watch his videos.

교육 기관: Jose M L

Dec 07, 2017

Needs a few corrections on the last week's assignment. Other than that great course. I recommend people to go deeper (no pun intended) in learning Tensorflow and Keras by self studying via other resources (books, videos, tutorials) since the programming material is too extensive to teach in a course like this which seems intended to master the basic concepts and the most important results in convnets. Thank to Andrew and the TAs for an excellent course. See you all in the Sequence Models and last course!

교육 기관: Kai-Peter M

Oct 28, 2019

Great course!!! The best online course I have ever taken! I enjoyed almost every day I participated in that course, really an educational treasure! It is so comprehensive and detailed at the same time. Due to the good presentation of the topics it was really understandable. The only thing I would wish for future participants: please make it easier to get the complete Jupyter notebook environments from the Coursera platform once completed. I spent a lot of time here - even after consuming the related blogs.

교육 기관: Matthew B

Jan 01, 2018

Great course. Brilliant overview of CNN with recent implementations. I understand the limitations of covering only so much material in 4 weeks. Wish the course could have gone deeper on training YOLO. I had to do this myself from the darknet website with some other tutorials. Something to consider, implementations of Unet and Mask RCNN may be even more useful for precise object masking/detection rather than bounding box in the future. May be worth mentioning these techniques as they develop further.

교육 기관: Shehryar M K K

Apr 30, 2018

This is the 4th course in the series of deep learning that I finished. It was very enjoyable. The topic is deep and the instructor referred to papers and their implementation as exercises. Inception networks and ResNet exercises were my favorite and I learned a lot from them. The other assignments were good but weren't enjoyable as the two mentioned above. I would suggest the instructor incorporates some reading materials in the course which can be tested in the quizzes. Thank you for making this course.

교육 기관: Aniruddha S H

Mar 31, 2019

Excellent course. This covers almost everything you need to know about computer vision. starting with how Kernels detect edges, how convolutions work all the way to Object detection, face recognition, style transfer. This also includes references to some important deep learning papers which you must read. Programming assignments really help to understand the concept. but, some assignments are not clear and dimensions are confusing. Successful submission is a relief :P. Overall an Exceptional experience.

교육 기관: Waleed A

Dec 01, 2017

As someone who is studying AI and Neural Networks for the first time, I can say that this course was a very enjoyable experience for me. The structure of the information content makes the learning experience all the more valuable, and makes the learner yearn for more. Compared to the previous 3 courses, this course gives a little more mobility in terms of thought process and problem solving by introducing Keras, and allowing the learner to play around with models. All in all, it was well worth the time!

교육 기관: Brian L

Nov 01, 2018

Great stuff! I have some background in image and signal processing and the mathematical properties of convolutions; so I it made sense to me that they would be useful in deep learning for image processing. However, that point was amplified for me when Andrew Ng showed how a convolutional layer compared to a fully connected layer: The idea that a convolutional layer was achieved through parameter sharing and masking (forcing parameters to 0) and was in a sense a form of regularization was eye-opening.

교육 기관: Youdinghuan C

Jan 13, 2018

This is an amazing course. The instructor Andrew thoroughly walked through the motivation, concepts, and implementation of Convolutional Neural Network. The programming exercises are very informative, easy to follow, and helpful in terms of reviewing concepts covered lectures. Quizzes are of moderate difficulty and are also a resource for content review. Case studies chosen in lectures are very interesting and relevant. I highly recommend this course, especially for those who are new to the field.

교육 기관: Michael L

Apr 29, 2020

Hardest course until now. Overall very interesting, however I think i lack some basic understanding of tensorflow concept. I would like to have more examples and explanations of it. Its just that its often unclear: this only defines the tensor, and here we evaluate it, and if I run it again, does it compute from the begging or it remembers the value, and so on. This maybe refers more to the previous course. And besides that, would be great to have some text summaries of the material. :) Thank you

교육 기관: Luis E R

Apr 03, 2019

Andrew's teaching is exceptional, he finds the right way to convey the necessary information for complex concepts, he does not skip them but strikes the right balance of not going too deep, however he does warn you in a way, that you need to study them on your own.

I think the course, will give you very strong foundations if you take time to understand what you are really doing and what the algorithms are doing.

After this I think you will require a lot of practice with several examples on you own,

교육 기관: Hari K M

Jan 19, 2018

Really good course but relatively tougher than the previous ones. Learnt a lot with best part being able to learn state of the art algorithms and implementations. Did felt kind of oblivious at times while doing the programming assignments but the discussion forums came in handy during those times. There are some issues with the grading of last programming assignment which I think will be resolved soon. I definitely recommend this course to everyone who wants to specialize in neural networks.

교육 기관: Dhritiman S

Dec 09, 2017

The material in the course was very good. Andrew Ng is a fantastic instructor and is able to convey concepts in the most intuitive way.

This course would be perfect, but for the problems with the last two assignments (Face Recognition and Style Transfer). There were errors in instructions and grader solution wouldn't match solution expected in the notebook. The only way to figure out how to pass the assignments was to dig into forum posts where information was provided in a haphazard way.

교육 기관: Paulo A F

Nov 09, 2017

Great course. It has all the main state-of-the-art approaches. I just missed dealing with 3D data (RGB-D and point clouds). I believe the programming assignments get better as the course progresses because they get more demanding.

This is a great overview course. I suggest anyone interested in deep learning vision to start with this course and then move on to implement a CNN in tensor flow form scratch using one of many tutorials online.

Thank to the team for this great course!

Best regards,

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: Martin B

Sep 01, 2019

As with all the other courses by Andrew Ng, pacing and presentation are perfect. Learning this material is highly rewarding. Programming assignments are clear and accessible, although a little bit more thorough introduction in the use of Keras and Tensorflow wouldn't hurt in some cases. I found myself pretty deep in the documentation of both libraries - although that might be part of the intended learning process. Highly recommended! - Thanks to professor Ng for making this available

교육 기관: Camilo G

Jan 14, 2020

Curso excelente. Da todos los detalles más importantes sobre redes convolucionales, incluyendo las matemáticas que las hacen funcionar (incluso explica backpropagation en un ejercicio opcional) y cuáles son y cómo funcionan las aplicaciones más importantes. Omite una que otra cosa, por ejemplo cómo aplicar vectorización a todos los ejemplos de entrenamiento, y de vez en cuando durante los videos secciones de audio se repiten por alguna razón, pero mayormente está bastante completo.

교육 기관: Mihai L

Feb 19, 2018

This course is still amazing. Finally understood what CNN's are for and how to use them.

This is the first time in deeplearning.ai specialization that I had to consult the forums. by far implementing in low level code convolutions (first asignment) was the most difficult part.

Spent more time then with the other courses but it was time well spent. Again Andrew NG delivers a good course.

The minor editing problems in videos are the only issue that might be raised with this course .

교육 기관: Andrew K

Dec 29, 2017

The entire course is great, from the lectures by Andrew Ng, to the homework assignments, and the TA's help on the forums. The really terrible part of the course is the coursera grader, which I had to hack for 3+ hours just to pass an assignment. I dont wanna dink the review for this because the class itself is wonderful. But please fix those technical issues. So the 5 stars come from averaging 10 stars from the course itself, and 0 star for coursera technical issues. :-)

교육 기관: Omar S M

Sep 16, 2019

This is an excellent course in which Professor Andrew Ng explains the concepts of convolution, pooling and convolutional neural networks very well. Also the various advanced convolutional network architectures and various applications in computer vision are discussed in an excellent manner along with references to the research papers on which the content is based. The programming assignments are also excellent and really help you learn the principal concepts and techniques.

교육 기관: HEF

Jun 02, 2019

Before taking this course, I thought computer vision had a difficult learning curve. After taking it, I found that many difficulty materials are omitted so that I could learn without too much pressure. While I could still look into algorithm details because many papers are recommended. The programming assignments cost me a little more time than the previous courses, but bring so much more fun! I felt quite proud of myself when I successfully built the CNN in my assignments.

교육 기관: Ashwini J

Jan 01, 2020

Thanks to Andrew Ng and team for putting together great content around Convolutional Neural Network. This is a fairly complex course, I needed to go beyond content provided in this course, specifically around understanding dimensions resulting from a convolution operation applied on an input image. This could be because it is hard to imagine a 4-d object. Otherwise, good content put together, assignments are good and useful starting point for projects in actual practice

교육 기관: Shyam C N

May 17, 2020

This course was one of the better ones in the specialization. I enjoyed it very much. The assignments are a bit more practical, and require some thought while debugging. Although some TensorFlow experience from Course 2 is expected and useful, this course requires some additional reading of the TF and Keras manuals. My only suggestion to the development team would be that they improve the NST assignment's introduction of TF methods like assign() and InteractiveSession.

교육 기관: Selina N

Mar 20, 2020

It's an exciting course. I find very interesting to learn object detection, facial expression and face recognition. The concept of neural style transfer is easy to understand and funny to generate image to absorb the style from another image. The explanation is useful. One improvement is some assignments only import the trained models with extra source code. It would be better for students to build by themselves to go through the whole model development step by step.