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Отзывы учащихся о курсе Convolutional Neural Networks от партнера deeplearning.ai

4.9
Оценки: 22,258
Рецензии: 2,717

О курсе

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....

Лучшие рецензии

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.

EB

Nov 03, 2017

Wonderful course. Covers a wide array of immediately appealing subjects: from object detection to face recognition to neural style transfer, intuitively motivate relevant models like YOLO and ResNet.

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1–25 из 2,682 отзывов о курсе Convolutional Neural Networks

автор: Gyuho S

Apr 25, 2019

This course is definitely tougher than the first three courses. Challenging but worth it.

автор: David B C S

Dec 17, 2018

Great course, easy to understand and very useful. The explanations are very clear, as is expected from the professor. The purpose of the course is for you to have a practical comprehension of CNNs, it will give you the necessary tools to implement you own networks, but it will not get into the specifics of each model. Nevertheless, all of the resources are referenced, which makes it very easy for you to dig deeper on any specific topic covered on the course.

автор: Aleksa G

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.

автор: Farzeen H

Jan 12, 2019

Amazing! Feels like AI is getting tamed in my hands. Course lectures , assignments are excellent. To those who are not well versed with python - numpy and tensorflow , it would be better to brush up.

автор: fabrizio f

Dec 17, 2018

Very good however most of the effort is applied in learning and applying programming (tf, Keras) than actually thinking about the DL models and practicing different scenarios.

автор: Cosmin D

Jan 04, 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.

автор: Michael J

Jan 02, 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.

автор: Tian Q

Jan 01, 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.

автор: Markus B

Dec 05, 2018

Great course. The only improvement I'd wish is to get a better introduction to the concepts of Tensorflow and Keras.

автор: Basile B

Apr 30, 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... =(

автор: Liam A

Jun 26, 2019

Very challenging and informative.

автор: 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.

автор: Dimitry I

Jun 25, 2019

Another great course in the in the Deep Learning specialization. Lots and lots of data is presented in small steps that make it easy to assimilate and apply. Thank you to Prof Ng and the rest of the team!

автор: Alejandro M

Jun 25, 2019

Great Course lectures and great resources!

автор: JUAN S G R

Jun 24, 2019

It gives you the essentials of modern Deep Learning, I'm very very grateful

автор: ABHISHEK K

Jun 24, 2019

This was very deep and more eye opening course where I had to go through yolo, resnet and keras. Much recommeneded.

автор: Dani V

Jun 24, 2019

The course material is excellent. The professor explains perfectly. The value of the information delivered is great.

автор: Hyunseok

Jun 24, 2019

It was awesome! You will never regret to choose this course!!

автор: SUNPILL K

Jun 23, 2019

perfect

автор: Eric C

Jun 23, 2019

Awesome. This course was much more dense than the other ones, there is so many areas to review. Since this course is about my favorite subject, I will need to pause and rework on each individual points and associated papers (yolo, nst, similarity learning) which will probably take me weeks... Prof Andrew is the best

автор: Martin K

Jun 23, 2019

Great course! I've learned a lot about Convolutional Neural Networks from this course!

автор: 胡伟澎

Jun 23, 2019

I love the Easter egg presented by Andrew, it bring so much fun with the course!

автор: Ayzhamal Z

Jun 23, 2019

Great course. Andrew Ng is amazing!

автор: Renu K

Jun 23, 2019

Amazing course. Gives a very simple yet detailed understanding of deep learning with images

автор: Ravi K Y

Jun 22, 2019

awesome