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Отзывы учащихся о курсе Convolutional Neural Networks от партнера

Оценки: 40,120
Рецензии: 5,312

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 дек. 2019 г.

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.

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

Фильтр по:

451–475 из 5,284 отзывов о курсе Convolutional Neural Networks

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

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.

автор: Carlos V

19 янв. 2018 г.

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.

автор: Yun-Chen L

19 мая 2020 г.

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.

автор: Thota m s s

3 нояб. 2019 г.

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

автор: Vincenzo P

20 мая 2018 г.

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.

автор: Shiro K

31 янв. 2018 г.

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. :)

автор: Markus L

20 нояб. 2017 г.

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.

автор: Veeraraghavan N

14 июня 2020 г.

The course is really good with in-depth explanations of the concepts in a clean, clear and precise manner that is both easy to understand and implement. The programming assignments are fun to complete and test out. Highly Recommended!

автор: Raymond S M

25 июня 2019 г.

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.