Chevron Left
Volver a Convolutional Neural Networks

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

4.9
estrellas
31,448 calificaciones
3,956 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

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.

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.

Filtrar por:

351 - 375 de 3,927 revisiones para Convolutional Neural Networks

por Amey N

Dec 15, 2019

The course brilliantly explores the crux of computer vision and art generation by indulging the learner in hands-on experiences of significant applications of ConvNets such as face detection/verification as well as neural style transfer.

por Eamonn G

Sep 04, 2019

Five stars for an overall very good course. Professor Ng does a masterful job of explaining and providing the key insights into how state of the art convolutional neural networks work and how they can be applied in some really cool ways.

por Karan M

Nov 13, 2018

A very wonderful course! A must for people who want to enter the field of Computer Vision using Deep Learning. Core fundamentals are taught very clearly such that after doing the course, student can venture into the field on his/her own.

por Lucas B

Apr 07, 2018

Substantive and relevant, yet clear and straightforward. My only recommendation would be to add some GitHub links and/or optional assignments in order to give slightly more open-ended assignments that require more than filling in blanks.

por Carlos V

Jan 20, 2018

Another excellent course by professor Andrew Ng and Coursera, the level of explanations and material are excellent, the detail in those Jupyter Notebooks is fantastic, I highly recommend this course to anyone interested in Deep Learning.

por Yun-Chen L

May 19, 2020

This course had more technique skills, like CNN. maxpool. Residual network. triplet loss. YOLO model. style transfer. I like assignments because it give you some research papers and examples in the real world, that will make you better.

por MOHAN S S T

Nov 03, 2019

Its the best course where you can practically implement your own learning algorithms the best thing was I implemented a famous ResNet on my computer and that great . Anyone interested in CONVNETS should definetly try this great course

por Vincenzo P

May 21, 2018

Great course! Classes of Andrew Ng are, as usually, crystal clear about necessary theory and full of precious hints for efficient implementation of CNN. I recommend it to everyone seriously interested in Computer Vision advanced tasks.

por Vincent L

Jan 31, 2018

Hardest of the 4 so far. There's more autonomy required in programming and shape calculations require really understanding how ConvNets work. But the more difficult it is, the more worthwhile and non-trivial the achievement becomes. :)

por Markus L

Nov 20, 2017

Excellent overview of CNNs including practical exercises with appropriate level of details. Gained good understanding what one can accomplish with CNNs and where to start. Also gives good idea of practical implementation costs of CNNs.

por Raymond S M

Jun 25, 2019

I found this to be an excellent introduction to convolutional neural networks. I was already very familiar to convolution but I could see that if I wasn't it would have been clear. All concepts were explained well and I learned a lot.

por David R V O

Mar 11, 2019

I think this course is excellent and I'm already applying the skills I've learnt from it to my current research. I would have preferred a little bit more focus on the theorical part of ConvNets, especially backprop. 100% recommended.

por Zifei S

Feb 20, 2019

Very clear lectures and hands-on experience to gain lots of experience with CV problems and cutting-edge models. I'm an NLP engineer and this course gives a great intro to DL for CV. IMHO it's one of the greatest course in the series.

por Yuezhe L

Nov 20, 2018

This is an immensely helpful class. I have been wanting to learn imaging processing and machine learning, and this class helps me get started. Using what I learnt from this class, I was able to implement CNN to help my own research.

por Taras M

Jul 29, 2018

The most interesting course in the whole deep learning specialization, a lot of practical cases and much closer to the deep learning state of the art. Kudos for face recognition and neural style transfer (yolo is super cool as well)

por Li W Y

Jan 08, 2018

This is a great course which make me know how to do computing vision and neural style transfer (which is something I thought amazing before). Although the course is a bit difficult, it is interesting and useful. Hope you enjoy too.

por Gautam D

Jan 11, 2019

Wonderfully explained! Andrew and team have been kind enough to provide all the important papers and documentation required too. Very well laid out course. Can't wait to finish the 5th and final course! Thanks team deeplearning.ai

por Bhaskar G

Sep 26, 2018

An optional course on Tensorflow/Keras and their comparison with other prevalent frameworks would have given a nice touch. I realized that lot of handholding is needed in assignments just because the basics if TFlow are not clear.

por 胡帆

Feb 28, 2018

Excellent course! And the programming assignment is necessary if you want to know deep learning deeply, the video is shallow and it is more like an introduction to deep learning . Anyway, Andrew Ng is absolutely a great teacher!

por Jack Q

Jan 31, 2018

This course offers quite plentiful materials. I learned lots of models whose performances are state-of-the-art. Brief and intuitive description of these models helps me a lot when reading the corresponding research papers. Thanks!

por Ernesto N S

Dec 20, 2017

I love how the content of this course is structured. Also love the fact that all the weeks contents can be found within the exercises themselves. Thank you to all the people that worked so hard to deliver this exceptional content!

por Siddharth P

May 24, 2020

A very well presented course that covers a good breath on different types of CNN model to date. The exercises are good, given the computational limitations, it is understandable why most of the exercises used pre-trained weights.

por Makarand D

Mar 19, 2020

A more consistent Keras or Tensorflow workflow would be good. I passed all the assignments but still feel unclear abotu Keras and TF workflow. But that will come with practice. Great teaching in terms of conceptual understanding.

por Zhoutian F

Apr 15, 2018

The course is really helpful to beginner. However, I really suggest to add more introduction of modern CNN networks to this course, such as R-CNN for semantic segmentation. Really appreciate Prof. Ng and Coursera for this course.

por Xim B

Dec 20, 2017

Step-by-step convolutional networks are presented. It is excellent learning to construct the convolutional networks in the lab. I read too much about these type of neural networks, but no one has me shown before how to build them