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Отзывы учащихся о курсе Convolutional Neural Networks от партнера

Оценки: 40,446
Рецензии: 5,359

О курсе

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....

Лучшие рецензии


11 июля 2020 г.

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


12 янв. 2019 г.

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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451–475 из 5,334 отзывов о курсе Convolutional Neural Networks

автор: Diego A P B

6 мар. 2018 г.

CNNs are one of the most valuable types of Neural Networks that are being used nowadays, and this course is a great introduction to both the logic and math behind the algorithms. For sure one of the many stepping stones to master this subject!

автор: GurArpan S D

6 дек. 2017 г.

I would not consider this course as on par with the prior courses on this specialization. It seemed rushed and there wasn't as much complex thinking we had to do on the assignments. Hopefully, you make it better over time! 5 stars nonetheless!

автор: Krisiphala S

12 окт. 2020 г.

A very good course, I know it is not a programming course but I think more programming assignments would let us understand better on what the network is actually doing, maybe some mini exercises in between lecture. But overall a great course!

автор: Nguyễn H T

9 авг. 2019 г.

I think this course help me much in gaining intuition about Convolutional Neural Network such as what is it, how it works. Additionally, the program assignment is really helpful and good. I can do with many other famous Convolutional Network.

автор: Willismar M C

22 мая 2018 г.

A very nice introductory course in Convolution Networks and some of the main algorithms today. Its a really start to become a practitioner in Deep Learning and keep focus on architectures and experiments. I really enjoyed every lecture of it.

автор: Shridhar A H

18 окт. 2020 г.

Great course!!

Very deep explanation about convnets. This course has everything you need to know about Convolution Neural Networks. Programming assignment were quit tough but if you follow along with lectures you can complete the assignments.

автор: Luis A

23 апр. 2018 г.

Excellent course. However, watching professor Andrew Ng Stanford lectures (on YouTube) we get the essential without any heavy math but on the other hand we learn to apply cutting edge techniques so in balance I think the course is excellent.

автор: Xiaoyuan C

3 дек. 2017 г.

A great course for people to get a comprehensive overview of the CNN landscape in a relatively short time, with a saving of tons of time. With so much of information, I am completely overwhelmed, and need much time to digest them gradually.

автор: YeongHyeon P

23 мар. 2020 г.

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.

автор: CHEYU L

17 авг. 2019 г.

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!

автор: Sanket G

18 февр. 2018 г.

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!

автор: duke P

18 мая 2020 г.

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 :)

автор: Satvik -

3 мая 2020 г.

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.

автор: Ingrid A

25 нояб. 2017 г.

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.

автор: Muhammad A

20 июня 2020 г.

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.

автор: Doris P

26 мая 2020 г.

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.

автор: Momin A K

16 нояб. 2019 г.

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.

автор: Pedro B M

24 апр. 2019 г.

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.

автор: Gabriel V

12 нояб. 2018 г.

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.

автор: Ber L C

8 нояб. 2018 г.

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!

автор: Joaquin C D

17 июня 2018 г.

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.

автор: Amey N

15 дек. 2019 г.

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.

автор: Eamonn G

3 сент. 2019 г.

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.

автор: Karan M

13 нояб. 2018 г.

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.

автор: Lucas B

7 апр. 2018 г.

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.