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

4.9
stars
42,028 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

RK

Sep 1, 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.

OA

Sep 3, 2020

Great course. Easy to understand and with very synthetized information on the most relevant topics, even though some videos repeat information due to wrong edition, everything is still understandable.

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476 - 500 of 5,570 Reviews for Convolutional Neural Networks

By YeongHyeon P

Mar 23, 2020

The video lectures are very nice to understand. However, the programming assignment should be replaced with the recent version TensorFlow. The provided documentation of the assignment can not be accessed. All of the good lecture. Thank you.

By CHEYU L

Aug 17, 2019

This course is interest and useful. The most impressive one is "Neural Style Transfer algorithm", which makes me implement a lot of my own image and any other style to generate different interesting picture. I love it, and thank you Andrew!

By Sanket G

Feb 18, 2018

Excellent class, Andrew Ng is a legend in teaching concepts in a methodical and step-by-step way. The programming assignments can take a while to figure out how to clear the grader, but in terms of teaching materials - I'd say its the best!

By duke P

May 18, 2020

I really enjoyed in learning every single bit of information throughout this course. I hope that I will soon have the opportunity to work on a real commercial project for a startup or a company and use my new gained skills and knowledge :)

By Satvik -

May 3, 2020

The course is awesome and well curated by the assignments. But its a bit unguided after YOLO, as i get confused whats going on. All the things were getting messed up and even the instructions in programming assignments were also confusing.

By Ingrid A

Nov 25, 2017

This course was quite challenging but rewarding. I learned how to implement state-of-the-art algorithms. As always, Professor Ng is a great teacher. His team obviously puts a lot of effort into making these classes the best they can be.

By Dr M V G

Feb 22, 2023

I thoroughly enjoyed Andrew's style and delivery. The programming exercises were also very useful to learn from a practical point of view. I also felt the quizzes were a good way to ensure the content from the lessons was well understood.

By Muhammad A

Jun 20, 2020

It was a nice course that helps to understand how filters extract features from images that would in result help to understand the working of Convolutional Neural Networks and why it performs amazingly in Computer Vision or with images.

By Doris P

May 26, 2020

It was a great course, extremely useful, and examples are interesting. I have to say it was harder than I expected, and despite the time invested, I feel it will take some time for this all information to settle in. But, enjoyed doing it.

By Momin A K

Nov 16, 2019

This course has enabled me to develop the core concepts of convolutional neural networks. I enjoyed both the lectures and the assignments. The assignments were very helpful in terms of strengthening the concepts I learned in the lectures.

By Pedro B M

Apr 24, 2019

ConvNets is an amazing topic. The course has strong hands on characteristic, with nice intuitive explanations of every algorithm. I particularly liked the choices for the applications and the nice recommendations for the reference papers.

By Gabriel V

Nov 12, 2018

The course requires basic knowledge of neural networks. The course gives very good overview how convolutional NN works, what are the capabilities and even with some hands-on examples the course gives confidentiality to build own projects.

By 도준Mark

Nov 8, 2018

Learnt a tons about convolutional neural networks and computer vision algorithms. Thank you very much professor!!! Hope to see many more of your courses being offered in Coursera, especially those about Machine Learning and Deep Learning!

By Joaquin C D

Jun 17, 2018

Es un curso muy interesante, te introduce y te muestra el mundo del reconocimiento de imagen y las redes neuronales aplicadas a la imagen. Desde sistemas de detección de vehículos, hasta filtros artísticos para imágenes. Muy recomendable.

By 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.

By Eamonn G

Sep 3, 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.

By 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.

By Lucas B

Apr 7, 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.

By Carlos V M

Jan 19, 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.

By 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.

By Kolappan P

Dec 29, 2023

Simply superb!!! I love both the content, style and the enthusiasm by Andrew Ng!!! Very grateful to be able to get this level of quality teaching remotely, highly recommend for anyone wanting to move to ML space from other domains.

By Thota m s s

Nov 3, 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

By Vincenzo P

May 20, 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.

By 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. :)

By 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.