Chevron Left
Retour à Convolutional Neural Networks

Avis et commentaires pour d'étudiants pour Convolutional Neural Networks par deeplearning.ai

4.9
étoiles
37,078 évaluations
4,822 avis

À propos du cours

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....

Meilleurs avis

AG
12 janv. 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
1 sept. 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.

Filtrer par :

26 - 50 sur 4,773 Avis pour Convolutional Neural Networks

par Ralph R

27 avr. 2019

I think it's a good idea to remove repeated parts in the videos. Also, put all pieces toguether to give a better overview of the object detection solution

par Basile B

30 avr. 2018

IoU validation problem is known but nothing as been done to resolv it

video editing problem

unreadable formula in python notebook for art generation (exemple :

\(J_{style}^{[l]}(S,G) = \frac{1}{4 \times {n_C}^2 \times (n_H \times n_W)^2} \sum _{i=1}^{n_C}\sum_{j=1}^{n_C}(G^{(S)}_{ij} - G^{(G)}_{ij})^2\tag{2} \)

What append ? that was great so far... =(

par Shibhikkiran D

8 juil. 2019

First of all, I thank Professor Andrew Ng for offering this high quality "Deep Learning" specialization. This specialization helped me overall to gain a solid fundamentals and strong intuition about building blocks of Neural Networks. I'm looking forward to have a next level course on top of this track. Thanks again, Sir!

I strongly recommend this specialization for anyone who wish get their hands dirty and wants to understand what really happens under the hood of Neural networks with some curiosity.

Some of the key factors that differentiate this specialization from other specialization course:

1. Concepts are laid from ground up (i.e you to got to build models using basic numpy/pandas/python and then all the way up using tensorflow and keras etc)

2. Programming Assignments at end of each week on every course.

3. Reference to influential research papers on each topics and guidance provided to study those articles.

4. Motivation talks from few great leaders and scientist from Deep Learning field/community.

par Zeyad O

15 avr. 2020

I'm Zeyad, an undergraduate of Computer Engineering at Alexandria University in Egypt.

Taking this course really helped me to learn and study this field and also to implement it. It helped me advance in my knowledge. This course helped me defining Deep Learning field, understanding how Deep Learning could potentially impact our business and industry to write a thought leadership piece regarding use cases and industry potential of Machine Learning.

This specialization helped me identifying which aspects of Deep Learning field seem most important and relevant to us, apparently they were all important to us. Walking away with a strong foundation in where Deep Learning is going, what it does, and how to prepare for it.

Deep Learning specialization helped me achieving a good learning and knowledge about that field.

Thank you so much for offering such wonderful piece of art.

Best Regards,

Zeyad

par Michael J

2 janv. 2019

A short (but cogent) overview of CNNs with a ton of references to read through and much more interesting assignments (than previous courses). I really enjoyed this course, I got a ton of exposure from it.

par Devjyoti M

22 avr. 2019

This is one of the best courses for CNNs. This gives a very deep understanding of the concepts and helps to understand the brains behind the CNNs and their working in application based environments.

par Daniel G

13 févr. 2018

Too much hand-holding during assignments, although still very good directions. Obviously the issue with the final programming assignment needs to be addressed. Fantastic lecture material, as always.

par Tian Q

1 janv. 2019

Excellent introductory course for CNN. The basic ideas and key components are explained clearly. Coding assingments helped me understand the algorithm to every little detail.

par Cosmin D

4 janv. 2019

Good content, videos have the occasional editing hiccups that also affect other courses in this specialisation. Assignments could be a little bit harder but do a reasonable job at familiarising with useful deep learning frameworks.

par Sai B A

9 oct. 2019

The course content is great, I felt link the programming assignments should have more information on running the Tensorflow sessions and (optional )information for people who are not familiar with Tensorflow would be great.

par Chris A

10 juin 2018

Great course - only thing keeping me from giving 5 stars is the consistent problem with the notebooks/grader.

par 小贱贱

14 mars 2018

assignment of week 3 has a bug about calculation of iou

par Moustapha M A

29 janv. 2018

I am a bit disappointed with this course , despite best efforts by Andrew. There is serious lack of rigor and while it is exciting to see things work , there is very little science to give us a methodical reason of why it works . In ConvNet we see the input data, a multi dimensional matrix get reduced in size using filtering and convolution operation techniques. From a mathematical point of view, this is clear and can be formalized but it is not clear why this process causes the ability to identify edges in a picture and evolve as we go deeper into the convNN to the real picture etc...

It seems to me this more like an alchemy rather then a rigorous scientific approach and this is why it was difficult to follow the exercises from the material of the course . I have to put concerted efforts to understand the literature which itself was not easy as it lacked rigorous mathematical and scientific approach ( why we have to increase the channels by multiples as we go deep into the conVNN ? etc...) . It seems to me the whole field is at its infancy with trials and errors - and more formalized approach is needed.

par Jacob K

31 août 2019

Great content, but this module gets far too buggy. The videos stutter and repeat as if they were going to be edited butt never were, and the programming exercises are so sloppy. The first exercise says, welcome to the second exercise, and congratulates you for finishing the course, even though the second assignment remains, that also says welcome to the second exercise! Loading a model hangs forever on one, and running the GAN crashes the kernel on the other. People in the forum have been complaining since at LEAST last year, and it's still buggy. This course content is great, but very shoddily put together compared to the rest. I am literally scared what week 5 will be like. Just clean it up guys. Hire an temp!

par Younes A

7 déc. 2017

Wouldn't recommend because of the very low quality of the assignments, but I don't regret taking them because the content is great. Seriously the quality of deeplearning.ai courses is the lowest I have ever seen! Glitches in videos, wrong assignments (both notebooks and MCQs), and no valuable discussions on the forums. Too bad Prof Ng couldn't get a competent team to curate his content for him.

par Bilal B

19 août 2019

I know I am giving 2 stars :( but unfortunately this course was bit difficult and I don't know why Professor didn't first gave few fundamental concepts of computer vision. It's just my opinion maybe I'm wrong, maybe I'm right. But honestly we should have gone through some basic C.V. so that few students like myself can get a better understanding rather than directly diving into use of DL in CV.

par Milica M

10 mai 2020

boring and uninformative; could use improvement and some rehearsal before giving a lecture; boring and unorganized delivery; slides are horribly unorganized and boring; often times very confusing and hard to follow; should minimize the number of times the instructor references basic math and should use that time to motivate the concepts and applications

par Weinan L

12 mars 2018

This may be the most enjoyable course in the whole series so far. It is intuitive and fun, and the results are tangible. Very practical.

Inevitably, due to the complexity of CNN, we have to rely on frameworks such as TensorFlow/Keras, etc. to do the coding, and they are covered in this course as well. Not very deep, but sufficient. Wish they may pick PyTorch in the future as well.

The notebook and grading systems sometime have issues though. You may think you submitted the right data but actually the server side won't think so. Hard lessons learnt are: a) save the original ipynb before coding, so you can always rollback in case notebook messed up; b) save a checkpoint before submit, this will force saving and ensure you submitted the latest data, otherwise, it may submit incomplete data - some cells may still have very old data even you modified a lot; c) open anther local Jupyter notebook to experiment and mess around, with interactive TensorFlow exception, but pay attention to the expression with random sequences, when you call eval() the second time, they may have totally different value even you reset the seed upon each cell, eval() will invoke your expression again which will consume more data in the random sequence; d) never use iPad to complete your noetbook coding, :-).

par Alan L V J

4 déc. 2017

Este curso introductorio es estupendo para aprender desde cero sobre convolutional neural networks.

Professor Andrew Ng, makes very comprehensible the content of the course.

Here why:

-He decompose every element of CNN. Convolutions, 1x1 convolutions and pooling are very well explained, then by yourself can derive the dimensions of the output after applying these operations.

-He make notes on the fly for derive equations and explain the purpose of the equations. For me is much better that only show slides, because makes give me the oportunity to think of the equation before is show.

-Professor give you Intiition in every topic.

- He Make several examples of modern architectures of CNNs.Always write down in detail the architectures.

-Clear notation, uses the same notation in programming exercises

-Programming exercises are the best documente ones. This makes relatively easy to implement the exercises. If struggle with operations, they provide links to the documentation necessary.

Was an amazing course.

Althogth I always think CNNs were some what difficult and sometimes tedious topic (because of convolution and pooling arithmetic, and the use of "volumes" instead of matrices), this course make all clear and natural.

Thanks to the instructors for they hard work.

par Neil O

4 juil. 2018

If you're not particularly interested in image identification and recognition, there is still reason to do this course. CNNs are amongst the most advanced areas of DL and understanding the concepts can help develop intuition about how to solve DL problems in other domains. I greatly enjoyed this course. As with all of Andrew Ng's courses, the explanations are clear and help develop intuition. This course seems to have more references to academic papers than the others and Andrew is encouraging and helpful in guiding the student to the accessible and relevant sections of the papers.The exercises are instructive and not too challenging. Most of the challenges I had were due to my own programming errors and occasionally an error in how the exercise is set up [make sure to use the most recent version of Jupiter notebooks]. One exercise in Week 4 (Neural Style Transfer) does assume more Tensorflow knowledge than the other exercises. Recommend brushing up on Tensorflow before trying this and using the discussion groups which are helpful for debugging suggestions.

par Plusgenie

27 août 2018

Coursera 온라인 강좌 딥 러닝에 정말 감동 받은 점:

#1 정규 대학교나/대학원 가지 않고 온라인으로 싸게 배울 수 있다.

#2 아무리 어려워 보이는 학문이더라고, 관점을 정확하게 설명해주면 누군든지 쉽게 배울 수 있다.

즉 E=MC^2 같은 공식은 누구나 발견할 수 없지만, 누구가 쉽게 배울 수 있는 것이다. 학생이 모르면 선생의 잘 못이다!

#3 지식은 투명하게 공개되어야 한다. 공개되지 않는 지식은 특권계급을 만든다.

#4 학교를 떠난지 그렇게 오래되었지만, 여기에 다시 공부해보니 다시 청춘을 느끼게 해준다.

“This is a record of your time. This is your movie. Live out your dreams and fantasies. Whisper questions to the Sphinx at night. Sit for hours at sidewalk cafes and drink with your heroes. Make a pilgrimage to Mougins or Abiquiu. Look up and down. Believe in the unknown for it is there. Live in many places. Live with flowers and music and books and paintings and sculpture. Keep a record of your time. Learn to write well. Learn to read well. Learn to listen and talk well. Know your country, know the world, know your history know yourself.

Take care of yourself physically and mentally. You owe it to yourself. Be good to those around you and do all of these things with passion. Give all that you can.Remember, life is short and death is long.”

– Fritz Scholder

par Akash B

31 mai 2019

I would highly recommend this course as learning from basic stratch to deepen your understanding about the subject topic, Although i found it very hard to solve the assignments because i was not on the track of tensorflow.

I would also recommend to take cs20 class by stanford which teaches tensorflow very well or you can refer the youtube videos for tensorflow also. The key thing is whatever you study you have to keep coming back to look at the assignments what you've done , play with it, understand it, and see how you can relate this on theory.

The video lectures is pretty striaght forward, not much mathematical jargon, but its intermediate level of sort, but i recommend to watch atmost 5 times every video if you didn't get through once, don't rush, take pen and paper and also write. You can also refer medium articles which are well curated from this course and provides a nice summary of overall what you've studied.

And if you got more time, just try to read some good papers. Thank you.

par Gustavo E P

28 janv. 2018

This has been the most exiting course within the Deep Learning specialization by deep learning.ai. It provides all the basic theoretical and practical knowledge to get you started right away with CNNs and its applications in computer vision, including state-of-the-arts algorithms for image recognition, face detection and neural style transfer. With the help of the well-designed and challenging programming assignments you can practice and reinforce what you have just learned by doing it yourself, while becoming familiar with popular NN frameworks such as TensorFlow and Keras. I strongly recommend to spend some time reading the papers and articles referenced in the lectures as those provides additional insight and background to the course material, as well as reviewing and experimenting with the code available from the course assignments and also from GitHub. All in all, another excellent course by Prof. Andrew Ng and his team!

par Sean O

25 mai 2020

Good set of courses on Deep Learning. Some small complaints / recommendations:

- Courses don't teach enough Keras & Tensorflow syntax to be completely stand-alone. If you take this course, you won't really be able to build your own DNN's unless you also take a separate Keras / Tensorflow course.

- Links to Keras documentation are broken -- they now take you to the general Keras homepage, not the specific command's page.

- In later courses, Andrew Ng's lectures are not edited. Starting around the 4th course, you start hearing Dr. Ng stop and repeat portions of the lecture, presumably intending the first attempt to be edited out in the future. Usually this is easy to ignore, but in some cases he repeats 30-60 seconds of lecture, which can be confusing.

- In the last course (sequence models), the text captions of Dr. Ng's lecture have a lot of mistakes, which is a little ironic for a course on speech-to-text

par Timothy

14 janv. 2019

Felt like I learned a lot about CNN. Perfect for introductory class I think. Applications include facial recognition/one shot learning. style transfer(my personal favorite) and object recognition/bounding box determination. I feel like it's perfect for me, having no previous experience with CNN(although convolutions in general are quite familiar to me). This is definitely for those with no previous experience with CNN or just small/moderate amount of it. You code up all the components necessary for CNN forward prop and a few pieces of the back prop to get an idea of what involved. After this the projects are in TensorFlow. I have no previous experience in TensorFlow but was able to do the exercises without to much difficulty. That said, reading some supplementary tensor flow materials would probably be helpful as I'm still a little hazy on it.