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Learner Reviews & Feedback for Convolutional Neural Networks by DeepLearning.AI

4.9
stars
42,232 ratings

About the Course

In the fourth course of the Deep Learning Specialization, you will understand how computer vision has evolved and become familiar with its exciting applications such as autonomous driving, face recognition, reading radiology images, and more. By the end, you will be able to build a convolutional neural network, including recent variations such as residual networks; apply convolutional networks to visual detection and recognition tasks; and use neural style transfer to generate art and apply these algorithms to a variety of image, video, and other 2D or 3D data. The Deep Learning Specialization is our foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology. It provides a pathway for you to gain the knowledge and skills to apply machine learning to your work, level up your technical career, and take the definitive step in the world of AI....

Top reviews

AV

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I really enjoyed this course, it would be awesome to see al least one training example using GPU (maybe in Google Colab since not everyone owns one) so we could train the deepest networks from scratch

YY

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Very exciting courses. Everything explained carefully but easily to understand. Great courses. This course really help me a lot on my journey to learn deeper about deep learning. Thank you very much.

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951 - 975 of 5,600 Reviews for Convolutional Neural Networks

By Bapiraju J

Nov 16, 2017

Teaching method on applications of convolutional neural networks is really great again. I am counting on next course sequence models or recurrent networks.

By Luciano D

Sep 23, 2020

Each course is even more awesome than the previous one!!!!

I hope deeplearning.ai release new specializations as great as these ones!!!

You guys rock!!!!!!!

By Nirmal B

Jun 8, 2020

The course not too descriptive but at the same time not superficial. It covered enough so that one can implement the CNN architectures with some framework

By SACHIN

May 14, 2020

Excellent course on CNN. The concepts taught were excellent. Programming assignments gave better insights into the concepts taught in theory.Enjoyed a lot

By Х. А Р

Apr 3, 2020

I found this course incredibly useful for my future job. I'm going to work with tasks which is related with images. So that, CNN is what is needed for me.

By Juan S S G

Oct 9, 2019

It is a pleasure to learn from such a Machine Learning hero as Andrew Ng. Thank you very much for sharing this knowledge on Convolutional Neural Networks!

By Johnathan C

Nov 5, 2018

Fantastic content as usual. Especially had fun with the car object detection and the neural style transfer. Can't wait for Sequence Models! Thanks again!!

By Joe H

Oct 11, 2018

clear yolo's tutorial

clear overfeat's explanation and sliding window's convolutional implementation

clear inception module 's description

Useful home works

By Nektarios K

Jun 9, 2018

One of my favorite classes so far! Loved it! You need this class if you want to master Deep Learning and see what popularized Deep Learning to begin with!

By Luca Z

May 18, 2023

If you have some knowledge about machine learning and deep learning, this course will place you in a good position to start your computer vision journey!

By Nguyen H T

Sep 27, 2021

This course is very helpful for me. It helps me to understand the CNN works and i can use it with my application. Thanks all the teachers and professors

By Sergio A D C A

Jun 15, 2021

A very interesting and helpful course to understand the basics of CNN, implementation, state of the art models, and very intuitive explanations. 5 stars!

By Vinat G

Jun 14, 2021

Probably the best course to get started with in the field of computer vision. Explanation of algorithms like YOLO, one shot learning, etc were very good.

By Reham E

Oct 10, 2020

Good course, it covers a lot of important topics in CNN.

I haven't got used to tf or keras yet, still struggling but the topics and concepts are ok to me.

By Rafał

Aug 11, 2020

Course gave me a lot of valuable knowledge. I think switching to tensorflow 2 would make code easier to understand. Especially in style transfer notebook

By yasser h

May 1, 2020

very Interesting courses allow students to understand deeply the concept of the most common architectures of the stat of art in CNN, thanks to professors

By Gema P

Apr 16, 2018

This course is great, it gives a general overview about architectures.

However, it is intense as it contains a lot of concepts that are extremely relevant

By Vishnu P S

Feb 7, 2018

Its a wonderful course, covered all concepts in CNN and its various applications in real world. It was a great experience. Thank you team for this course

By Fabian M

Dec 4, 2017

Great course, provides insights that I feel will really help in the real world, but is also a gentle introduction into the field of convolution networks.

By Leonid K

Apr 7, 2020

This course gives a significant boost in understanding of advanced ML concepts and practical skills related to image recoginition. Definitely recommend!

By Mohamad K

Dec 6, 2018

Its great course about deep learning computer vision. Make sure you take this course kuz DL and CV are both powerful Tech to process images and videos.

By Dave C

Jun 25, 2018

very good stuff - where neural networks really get interesting. and the projects were applied in areas where the amazing results were easy to visualize.

By Nishit K

Nov 20, 2019

I am deep learning practitioner and use CNNs very much for my work, but material here was very interesting and refreshing! Of course Andrew is awesome.

By Jiali H

Jun 18, 2019

the assignments have some bugs that sometime I could not find the "submit" button. Also, the links of "Hints" sometimes didn't direct to the right one.

By Chen S

Mar 18, 2018

Useful algorithms. The overviewing teaching style is quite useful in the sense that some of the ways of construction CNNs for specific use is provided.