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Opiniones y comentarios de aprendices correspondientes a Convolutional Neural Networks por parte de deeplearning.ai

4.9
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29,173 calificaciones
3,561 revisiones

Acerca del Curso

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....

Principales revisiones

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.

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201 - 225 de 3,528 revisiones para Convolutional Neural Networks

por George Z

Aug 29, 2019

Exceptional course taking you into the real world of deep learning by exploring new concepts and classical architectures like LeNet-5, AlexNet, VGG-16, ResNet, R-CNN, YOLO, FaceNet and Style Transfer that propelled deep learning in new heights. Loved every part of it (minus some hiccups with the grader).

por Mukesh K

Aug 29, 2019

The course is just awesome both in terms of content that is being taught in the lectures and the assignments. Though, I think the last week is not that much important for the industry purpose but definitely it is good for those who are interested in non-industrial use of tensor flow and neural networks.

por Ignacio H M

Mar 26, 2020

I finally understand YOLO! This course has the best material available on CNNs. Even though I come from a MSc in Computer Vision and Machine Learning, we didn't have enough time to fully cover 'complex' architectures such as YOLO. Thanks to this course I feel more up to date in the Deep Learning field.

por Scott H

Feb 05, 2018

I really enjoyed this course. I found it pretty approachable. FWIW, I'd taken Andrew's original ML class, but then skipped 1,2, and 3 of the new one (and jumped into 4) The course really holds your hand, so be prepared to force yourself to try some of this on your own to be sure you've understood it.

por Harsh B

Nov 06, 2017

This course is intended for ML learners who have background knowledge of NNs and want to enhance their scope of knowledge in CNNs. Prof. Andrew has been an amazing instructor. The material used in this course is mostly based on Tensorflow, so make sure to have a bit of prior knowledge in Tensorflow.

por Vidar I

Feb 13, 2018

This course really gets you started working with CNN. The only downside are the "bugs" in the assignments. My advise is to read the discussion forums before you do the assignment to know if there is a bug that you should know of before submitting.

Beside this minor bug, the course content is 5 star.

por Damian C

Mar 26, 2018

Really enjoyed learning more about the current state of the art of image recognition models. Although the structure needed can be at times overwhelming, the concepts are clear and implementation via open source packages make it feasible. Many thanks for making this available, keep the good work!

por Maciej F

May 08, 2019

Somehow, a bit harder than rest of the courses for me. I had problems with tracking dimensionality and tensorflow notebooks were hard and difficult to debug. I think it would be nice if tensorflow has its own as a course or 2 weeks maybe. But anyway the concepts explanations is great as always!

por Juan M E B

Apr 19, 2018

Excelent course. ConvNets are an eye-opening subject and the course explains the main concepts and applications in a simple way, indicating the source papers to understand better. I'd only ask for a couple of videos explaining in more detail backpropagation and the upload of the missing slides.

por Andrei N

Sep 21, 2019

The content, examples, assignments, and quizzes are thoroughly developed. All the courses of the specialization share the same notation and lead a student from basic concepts to complex ones helping to develop an intuition on each step. The best course on topic of Deep Learning one could find.

por benedikt h

Mar 10, 2018

great ! It is complex though, don't get fooled by the doable exercises - to really understand you can take several loops.

Imagine someone breaks up recent complex research paper into python notebooks for you and you get this delivered like a delicious food - this is how I feel about this class.

por Jun W

Dec 16, 2017

This is an excellent course. Although I've got 100%, there are still some details and intuitions need to be figured out. Maybe I will go over it again. And of cause, I'm looking forward for the fifth course. I wish the fifth is not the last course. We still need to know reinforcement learning.

por Dr. R M

Nov 07, 2017

Very informative lectures with simple explanations of what the algorithms are doing. The programming assignments are extremely detailed and well explained. This makes it very efficient and fun to learn the concepts of Conv Nets, Res Nets, the YOLO algorithm and so on in a short period of time.

por Nour A

Jan 07, 2019

The course explains topics I used to consider as "complicated" in a very clear and simple way. The videos and quizzes about theoretical concepts accompanied with programming assignments and extra reading material give solid understanding of the topic, its current trends, and future direction.

por Igor C C

Nov 05, 2018

I think that should have an optional video with the mathematics behind the convolution/cross relation, showing element-wise operations on a small volume with more than one channel. I know most people will find it boring, but i think it will make easier to fully comprehend the 1x1 convolution.

por Wei F

Dec 17, 2017

Really enjoyed learning this course. I'm a PhD Student in CS but neither in computer vision or NLP. I feel like these courses are sort of jump-starter, if you would like to learn more about DL and to be expert, there's a long way to go. However, this is really a good starter!! Thanks Andrew!

por Adarsh K

Feb 04, 2020

The best place to start Computer Vision! You'll get to implement state of the art Techniques in CV, most with practical Application. The quizzes are very well designed and test your concepts. You'll learn to use open source implementations and build on top of that as well. Wonderful Course!

por Dipo D

Jan 11, 2020

Like the other courses in the DeepLearing.ai certification, this course was also very crystal clear in teaching the concepts. Now, I can confidently read additional materials on Computer Vision. The assignments were also well thought out, kudos to all the TAs. Thanks for the awesome course.

por Rahuldeb D

Sep 04, 2018

Another exceptional course offered by Coursera. There are lot of new concepts to learn in this course.

Prof. Andrew Ng has explained each and every concepts in very lucid manner. I want to give a big thanks to Andrew Ng and all other teaching associates for offering such a beautiful course.

por Brandon W

Nov 24, 2017

Students had some technical issues throughout this course, with the autograder not correctly grading the assignments despite having all expected outputs correct. In time, I hope these issues can be fixed. However, given the level of instruction and quality of the course, still deserves a 5.

por Ajay S

Aug 30, 2019

really a great course for the image learning . i love this course well . and thanks for providing me the financial aid for the course . this will really help me to complete my research work on time .

Thnaks. for the profession Andrew Ng . for the designing and teaching a wounderful course.

por Sean C

Feb 20, 2018

Andrew Ng's explanation of Inception Networks greatly helped to demystify more complex-looking architecture diagrams in Google's Inception Net. This course helped a lot in being to be able to understand the base building blocks, as well as their arrangement & purposes within the network.

por Vincenzo M

Nov 26, 2017

Another super course from Andrew Ng and his team. As the other courses of the specialization, it presents the core concepts clearly. The exercise are foundamental to retain the concepts. As a suggestions, I would substitute the style transfer with an example more useful for real problems.

por Chun-Huang L

Mar 22, 2020

This course teaches CNN from the very beginning to the most details. Its examples and assignments are very impressive for people to know what happen in the model and how it works for many different applications. I can realize most CNN-related research papers after finishing this course.

por MOHD F

Jul 23, 2019

Convolutional Neural Networks by Andrew Ng is a Great course to start into the of CNN's Terminology for DeepLearning. This course provides me with a solid background in how the Convolutional Neural Networks works internally. Great lectures ........... Great everything thankyou Coursera