Chevron Left
Retour à Convolutional Neural Networks

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

4.9
étoiles
36,078 évaluations
4,654 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

RS

Dec 12, 2019

Great Course Overall\n\nOne thing is that some videos are not edited properly so Andrew repeats the same thing, again and again, other than that great and simple explanation of such complicated tasks.

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.

Filtrer par :

176 - 200 sur 4,607 Avis pour Convolutional Neural Networks

par Gabriele

Sep 11, 2020

I am really appreciating this specialization. The only thing that I would change is maybe focusing less on the matricial operations required e.g. in the loss function computation, and more on how to use Keras/TF at a higher level; at the moment, it would still take me a lot of time figuring out how to build a nn from scratch, or use an existing one, with these frameworks.

par Esteban C

Oct 08, 2019

Very good in-depth coverage of conv NN.

Just one little thing, week 4 Notebook assignments:

In style transfer code is not well explained how the train is actually working. In this case the input is set as a Variable instead of a Placeholder and this aspect is not mentioned or explained

In face recognition I still don't know how triple loss function is used during training

par 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.

par ayush k

Apr 28, 2020

Quite lucid and good introduction to CNN for beginners to intermediate level. I specially liked the links and discussions about different papers along the course that Andrew recommends to read. For some who has just hear about CNN, but knows about basic NN, this is a really good course to learn main things super fast and then proceed into their own personal topics.

par Kseniia P

Jun 30, 2019

Amazing course with clear explanations of how CNN works. Andrew gives you intuition and understanding of convolutions, pulling, padding, and explains the foundations in great detail, so you can understand state-of-art approaches and are ready to get hands on it. Thanks to the assignments' structure, you don't ever have to waste time on debugging irrelevant issues.

par Teye

Apr 06, 2018

I love this course. I only wish there was an opportunity to go step by step from looking at images, creating the dataset from the images, creating labels, applying a model, and then testing. This would help to answer a few questions that I have. However, when I read the papers recommended, I assume many of those questions will be answered, such as : why max pool?

par Umendra C

Jan 11, 2018

Best course on deep learning for computer vision! Convolutional networks can be tricky to understand, but Andrew has presented the material in a very easy to understand format. He starts with simple ideas and concepts and then build on them in an intuitive manner. Highly recommended course for anyone who wants to understand the deep convolutional neural networks.

par Michal M

Feb 11, 2018

Excellent course. Time well spent.

Simple explanations of difficult concepts.

I was able to download yolo v2 in pytorch, reconfigure it to use CPU on my Mac, and get it running on my webcam in 1h after completing Week3 assignment.

Told all my friends how awesome the course is.

Keep up the fantastic work.

Super stoked for part 5!!! and learning GANs and RI afterwards.

par Peter D

Nov 26, 2017

Great course from Andrew Ng, as always. The videos are superb in explaining some of the more recent algorithms and trends. And they provide good intuition on how to use them in your own work.

The only (minor) remark is that the exercises might not be that challenging for those that already have done some ML programming in the past.

But overall still 5 stars!!!

par Yan

Apr 15, 2019

I was always curious about the "CNN" concept every time it emerged in the news. Thanks to Prof. Andrew's mild explanation, now I get a straight intuition into it!

The assignments were very amusing in this section. It was not hard to get a pass with the help of forums, but understanding every step is more important I think. So I will come back to practice more.

par MONIL J

Jul 13, 2020

This is the best course for beginners as well as intermediates, to learn from basics and scratch up to the advanced of CNN. In this course, the fundamentals as well as all different CNN architecture and Face validation, recognition and neural style transfer has been covered and explained in very easy language.

Thank you Andrew Ng for such an amazing Course!!!

par Sherif M

Apr 19, 2019

Again a great course by Andrew Ng and his great team. Convolutional neural networks are the reason for the recent Deep Learning revolution or let's say better renaissance. Andrew does a great job in explaining the theory, math and application fields of CNNs while also telling about the history of recent advances in CNN algorithms and architectures.

Great job!

par Jaime M

Jun 15, 2019

As in previous courses, Andrew made understandable complex and abstract content. This course is by far more challenging than the 3 previous ones. Maybe not at the assignments as we make use of facilitating frameworks and helper functions, but to really follow what is happening behind... its another level compared to previous courses on the specialization.

par Adrien S

Dec 28, 2017

Great overall course, keep teaching please ! I learnt a lot. I have a Ms degree in Machine Learning but we didnt had the time to really learn about Deep Learning. I feel it was a great introduction to the field and I feel confortable now to get more in details about everything and read papers etc.

So thanks for that, and I can't wait for part 5 about RNN

par P M K

Dec 08, 2017

Hi

This was a really good course to see mini projects getting executed. It gave quite a lot of practical insights working on the problems. The only issue was that week 4 assignments had some bugs in code comments due to which people spend quite a lot of time debugging causing unwanted waste of tine and frustration. Please correct the errors.

Regards, PMK

par yuji w

Nov 16, 2017

nice program to learn about convolutional neural works. I always fascinated about convolutional networks and this course gives me the very nice introduction and sort of in-depth knowledge and first hand programming knowledge in this area. The instruction and nice and start from easy and slowly get you into the deep knowledge. Great course and nice work.

par Daniel C

Feb 01, 2018

This course covers the basics of convolutional neural networks. After you understand the materials covered in this course, you'll know how smart phone cameras auto focus on faces. You'll also learn the basic building blocks that powers self-driving technology. These are just two of the many cool concepts you'll learn in this course. Highly recommended!

par Vishaal K M

Jul 08, 2020

The programming exercises require much more attention than you think it does. Although it's required to only fill in the code in specific areas and not too much either, the foreword before each code section must be studied carefully if you are to build your own convnet. The video lectures are pretty straight forward, so there's nothing to worry about.

par Martín C

Jun 07, 2020

Unos de los cursos más didácticos que he realizado. Muy claras las explicaciones de Andrew Ng sobre todo con respecto a las capas que componen una ConvNet. ¡Lo disfruté! Recomendado.

One of the most didactic courses I have ever taken. Andrew Ng's explanations are very clear, especially regarding the layers that make up a ConvNet. Enjoy it! Recommended.

par Cem O

Apr 10, 2018

Just like the other courses in this series, this course was prepared with great care to optimize the learning outcome. Clear and motivating lectures, great selection of up-to-date methods and very illustrative examples. I would like to thank Prof. Andrew Ng and all the course staff most sincerely for designing and making available these great courses.

par Guangyu L

Feb 23, 2020

Very good learning experience. Prof. Ng gave a lot of insights about not only the CNN frameworks but also some real world working experience and hints which were very informative. For this one , I had very heavy work load during learning, I recommend people take it in a continuous manner, this helps you understand and connect every knowledge nodes.

par abhishek a

Aug 09, 2019

Excellent Course!! By doing the this course I am now feeling very confident in CNN. This course is very important for all whether they may or may not work in CNN/images. This fundamental learnt here can be used in other domains of deep learning.

Thank you deeplearning.ai Team for proving this wonderful course. It has opened new opportunities for me.

par Pin Z

Jun 24, 2018

This is a very good course to get to know the basic concepts of CNN and to start hands-on programming to implement CNN. Andrew's lecture gives very clear explanation of the principles of CNN, as well as introduction to state-of-the-art example network structures. The exercises help to build essential skills to program CNN using TensorFlow and Keras.

par Youssef H

Apr 10, 2018

I have really learned a lot from taking this course. During the course you will be exposed to the state of art deep learning architectures by understanding the theory behind them in lectures and then you will get to implement them in the assignments. I have taken the first three courses and I think that definitely this course is by far the best one.

par Elidor V

Aug 03, 2020

The course was simply great. It starts from the real basics of Convolutions, gives you all the needed theoretical background, then starts to focus on real-life scenarios. Also worth mentioning that is not a piece of cake. The given assignments are not easy in general, but after completing those the benefit will be more than clear. 100% recommended!